In this paper, we study the performance of baseline hidden Markov model (HMM) for segmentation of speech signals. It is applied on single-speaker segmentation task, using Hindi speech database. The automatic phoneme segmentation framework evolved imitates the human phoneme segmentation process. A set of 44 Hindi phonemes were chosen for the segmentation experiment, wherein we used continuous density hidden Markov model (CDHMM) with a mixture of Gaussian distribution. The left-to-right topology with no skip states has been (...) selected as it is effective in speech recognition due to its consistency with the natural way of articulating the spoken words. This system accepts speech utterances along with their orthographic “transcriptions” and generates segmentation information of the speech. This corpus was used to develop context-independent hidden Markov models (HMMs) for each of the Hindi phonemes. The system was trained using numerous sentences that are relevant to provide information to the passengers of the Metro Rail. The system was validated against a few manually segmented speech utterances. The evaluation of the experiments shows that the best performance is obtained by using a combination of two Gaussians mixtures and five HMM states. A category-wise phoneme error analysis has been performed, and the performance of the phonetic segmentation has been reported. The modeling of HMMs has been implemented using Microsoft Visual Studio 2005 (C++), and the system is designed to work on Windows operating system. The goal of this study is automatic segmentation of speech at phonetic level. (shrink)

This paper presents an in-depth analysis of tennis match scene classification using an adaptive Gaussian mixture model parameter estimation simulation algorithm. We divided the main components of semantic analysis into type of motion, distance of motion, speed of motion, and landing area of the tennis ball. Firstly, for the problem that both people and tennis balls in the video frames of tennis matches from the surveillance viewpoint are very small, we propose an adaptive Gaussian mixture model (...) parameter estimation algorithm, which has good accuracy and speed on small targets. Secondly, in this paper, we design a sports player tracking algorithm based on role division and continuously lock the target player to be tracked and output the player region. At the same time, based on the displacement information of the key points of the player’s body and the system running time, the distance and speed of the player’s movement are obtained. Then, for the problem that tennis balls are small and difficult to capture in high-speed motion, this paper designs a prior knowledge-based algorithm for predicting tennis ball motion and landing area to derive the landing area of tennis balls. Finally, this paper implements a prototype system for semantic analysis of real-time video of tennis matches and tests and analyzes the performance indexes of the system, and the results show that the system has good performance in real-time, accuracy, and stability. (shrink)

The research on Tibetan speech synthesis technology has been mainly focusing on single dialect, and thus there is a lack of research on Tibetan multidialect speech synthesis technology. This paper presents an end-to-end Tibetan multidialect speech synthesis model to realize a speech synthesis system which can be used to synthesize different Tibetan dialects. Firstly, Wylie transliteration scheme is used to convert the Tibetan text into the corresponding Latin letters, which effectively reduces the (...) size of training corpus and the workload of front-end text processing. Secondly, a shared feature prediction network with a cyclic sequence-to-sequence structure is built, which maps the Latin transliteration vector of Tibetan character to Mel spectrograms and learns the relevant features of multidialect speech data. Thirdly, two dialect-specific WaveNet vocoders are combined with the feature prediction network, which synthesizes the Mel spectrum of Lhasa-Ü-Tsang and Amdo pastoral dialect into time-domain waveform, respectively. The model avoids using a large number of Tibetan dialect expertise for processing some time-consuming tasks, such as phonetic analysis and phonological annotation. Additionally, it can directly synthesize Lhasa-Ü-Tsang and Amdo pastoral speech on the existing text annotation. The experimental results show that the synthesized speech of Lhasa-Ü-Tsang and Amdo pastoral dialect based on our proposed method has better clarity and naturalness than the Tibetan monolingual model. (shrink)

Acoustic emission technique is a common approach to identify the damage of the refractories; however, there is a complex problem since there are as many as fifteen involved parameters, which calls for effective data processing and classification algorithms to reduce the level of complexity. In this paper, experiments involving three-point bending tests of refractories were conducted and AE signals were collected. A new data processing method of merging the similar parameters in the description of the damage and reducing the dimension (...) was developed. By means of the principle component analysis for dimension reduction, the fifteen related parameters can be reduced to two parameters. The parameters were the linear combinations of the fifteen original parameters and taken as the indexes for damage classification. Based on the proposed approach, the Gaussian mixture model was integrated with the Bayesian information criterion to group the AE signals into two damage categories, which accounted for 99% of all damage. Electronic microscope scanning of the refractories verified the two types of damage. (shrink)

Computational models of meaning trained on naturally occurring text successfully model human performance on tasks involving simple similarity measures, but they characterize meaning in terms of undifferentiated bags of words or topical dimensions. This has led some to question their psychological plausibility (Murphy, 2002;Schunn, 1999). We present here a fully automatic method for extracting a structured and comprehensive set of concept descriptions directly from an English part‐of‐speech‐tagged corpus. Concepts are characterized by weighted properties, enriched with concept–property types (...) that approximate classical relations such as hypernymy and function. Our model outperforms comparable algorithms in cognitive tasks pertaining not only to concept‐internal structures (discovering properties of concepts, grouping properties by property type) but also to inter‐concept relations (clustering into superordinates), suggesting the empirical validity of the property‐based approach. (shrink)

Computational models of meaning trained on naturally occurring text successfully model human performance on tasks involving simple similarity measures, but they characterize meaning in terms of undifferentiated bags of words or topical dimensions. This has led some to question their psychological plausibility (Murphy, 2002;Schunn, 1999). We present here a fully automatic method for extracting a structured and comprehensive set of concept descriptions directly from an English part‐of‐speech‐tagged corpus. Concepts are characterized by weighted properties, enriched with concept–property types (...) that approximate classical relations such as hypernymy and function. Our model outperforms comparable algorithms in cognitive tasks pertaining not only to concept‐internal structures (discovering properties of concepts, grouping properties by property type) but also to inter‐concept relations (clustering into superordinates), suggesting the empirical validity of the property‐based approach. (shrink)

In this paper, we propose a person verification scheme using a novel combination of palmprint and speech. The crucial aspect of biometric based verification lies in its use of features in verification. Thus, in this paper, we propose two novel feature extraction methods for palmprint verification. The proposed methods are based on the Gaussian mixture model followed by subspace based approaches such as ICA I and ICA II, called independent component analysis I mixture (...) model and independent component analysis II mixture model for palmprint verification. The speech verification system uses sphericity based measurement for person verification. The proposed multimodal biometric system uses a fairly large database of 150 users. The robustness of the proposed methods is validated by introducing six different types of noises on palmprint images and Gaussian white noise with two different signal-to-noise ratios on speech samples. Then, fusion of palmprint and speech are carried out at match score level using the weighted SUM rule for both clean and noisy conditions. Finally, we compare the results of combined biometric with the results of individual biometric and also the results of the proposed methods against conventional subspace methods. (shrink)

Speaker recognition under mismatched conditions is a challenging task. Speech signal is nonlinear and nonstationary, and therefore, difficult to analyze under realistic conditions. Also, in real conditions, the nature of the noise present in speech data is not known a priori. In such cases, the performance of speaker identification or speaker verification degrades considerably under realistic conditions. Any SR system uses a voice activity detector as the front-end subsystem of the whole system. The performance of most VADs deteriorates (...) at the front end of the SR task or system under degraded conditions or in realistic conditions where noise plays a major role. Recently, speech data analysis and processing using Norden E. Huang’s empirical mode decomposition combined with Hilbert transform, commonly referred to as Hilbert–Huang transform, has become an emerging trend. EMD is an a posteriori, adaptive, data analysis tool used in time domain that is widely accepted by the research community. Recently, speech data analysis and speech data processing for speech recognition and SR tasks using EMD have been increasing. EMD-based VAD has become an important adaptive subsystem of the SR system that mostly mitigates the effect of mismatch between the training and the testing phase. Recently, we have developed a VAD algorithm using a zero-frequency filter-assisted peaking resonator and EMD. In this article, the efficacy of an EMD-based VAD algorithm is studied at the front end of a text-independent language-independent SI task for the speaker’s data collected in three languages at five different places, such as home, street, laboratory, college campus, and restaurant, under realistic conditions using EDIROL-R09 HR, a 24-bit wav/mp3 recorder. The performance of this proposed SI task is compared against the traditional energy-based VAD in terms of percentage identification rate. In both cases, widely accepted Mel frequency cepstral coefficients are computed by employing frame processing from the extracted voiced speech regions using the respective VAD techniques from the realistic speech utterances, and are used as a feature vector for speaker modeling using popular Gaussian mixture models. The experimental results showed that the proposed SI task with the VAD algorithm using ZFFPR and EMD at its front end performs better than the SI task with short-term energy-based VAD when used at its front end, and is somewhat encouraging. (shrink)

The performance of most of the state-of-the-art speaker recognition systems deteriorates under degraded conditions, owing to mismatch between the training and testing sessions. This study focuses on the front end of the speaker verification system to reduce the mismatch between training and testing. An adaptive voice activity detection algorithm using zero-frequency filter assisted peaking resonator was integrated into the front end of the SV system. The performance of this proposed SV system was studied under degraded conditions with 50 selected speakers (...) from the NIST 2003 database. The degraded condition was simulated by adding different types of noises to the original speech utterances. The different types of noises were chosen from the NOISEX-92 database to simulate degraded conditions at signal-to-noise ratio levels from 0 to 20 dB. In this study, widely used 39-dimension Mel frequency cepstral coefficient features were used, and Gaussian mixture model–universal background model was used for speaker modeling. The proposed system’s performance was studied against the energy-based VAD used as the front end of the SV system. The proposed SV system showed some encouraging results when EMD-based VAD was used at its front end. (shrink)

This paper implements the continuous Hindi Automatic Speech Recognition (ASR) system using the proposed integrated features vector with Recurrent Neural Network (RNN) based Language Modeling (LM). The proposed system also implements the speaker adaptation using Maximum-Likelihood Linear Regression (MLLR) and Constrained Maximum likelihood Linear Regression (C-MLLR). This system is discriminatively trained by Maximum Mutual Information (MMI) and Minimum Phone Error (MPE) techniques with 256 Gaussian mixture per Hidden Markov Model(HMM) state. The training of the baseline system (...) has been done using a phonetically rich Hindi dataset. The results show that discriminative training enhances the baseline system performance by up to 3%. Further improvement of ~7% has been recorded by applying RNN LM. The proposed Hindi ASR system shows significant performance improvement over other current state-of-the-art techniques. (shrink)

A vehicle-commodity matching problem is presented for service providers to reduce the cost of the logistics system. The vehicle classification model is built as a Gaussian mixture model, and the expectation-maximization algorithm is designed to solve the parameter estimation of GMM. A nonlinear mixed-integer programming model is constructed to minimize the total cost of VCMP. The matching process between vehicle and commodity is realized by GMM-EM, as a preprocessing of the solution. The design of the vehicle-commodity matching platform (...) for VCMP is designed to reduce and eliminate the information asymmetry between supply and demand so that the order allocation can work at the right time and the right place and use the optimal solution of vehicle-commodity matching. Furthermore, the numerical experiment of an e-commerce supply chain proves that a hybrid evolutionary algorithm is superior to the traditional method, which provides a decision-making reference for e-commerce VCMP. (shrink)

The sampling-based motion planner is the mainstream method to solve the motion planning problem in high-dimensional space. In the process of exploring robot configuration space, this type of algorithm needs to perform collision query on a large number of samples, which greatly limits their planning efficiency. Therefore, this paper uses machine learning methods to establish a probabilistic model of the obstacle region in configuration space by learning a large number of labeled samples. Based on this, the high-dimensional samples’ (...) rapid collision query is realized. The influence of number of Gaussian components on the fitting accuracy is analyzed in detail, and a self-adaptive model training method based on Greedy expectation-maximization algorithm is proposed. At the same time, this method has the capability of online updating and can eliminate model fitting errors due to environmental changes. Finally, the model is combined with a variety of sampling-based motion planners and is validated in multiple sets of simulations and real world experiments. The results show that, compared with traditional methods, the proposed method has significantly improved the planning efficiency. (shrink)

In this article, epoch-based dynamic features such as sequence of epoch interval values and epoch strength values are explored to classify infant cries. Epoch is the instant of significant excitation of the vocal tract system during the production of speech. For voiced speech, the most significant excitation takes place around the instant of glottal closure. The different types of infant cries considered in this work are hunger, pain, and wet diaper. In this work, epoch strength and epoch (...) interval features are used to represent infant cry-specific information from the acoustic signal. In this study, the proposed features such as epoch interval and epoch strength values are determined using zero-frequency filter-based method. Gaussian mixture models are used to classify the above-mentioned cries from the features proposed in this work. GMMs are developed separately for each of the cries using the proposed features. The infant cry database collected under a telemedicine project at the Indian Institute of Technology Kharagpur has been used for this study. In the first step, infant cry recognition accuracy is investigated separately using epoch interval and epoch strength features. To enhance recognition performance, GMMs developed using various features are combined through score level fusion techniques. The recognition performance using a combination of evidence is found to be superior over individual systems. (shrink)

Understanding and solving the psychological health problems of college students have become a focus of social attention. Complex networks have become important tools to study the factors affecting psychological health, and the Gaussian graphical model is often used to estimate psychological networks. However, previous studies leave some gaps to overcome, including the following aspects. When studying networks of subpopulations, the estimation neglects the intrinsic relationships among subpopulations, leading to a large difference between the estimated network and the real network. (...) Because of the high cost, previous psychological surveys often have a small sample size, and the psychological description is insufficient. Here, the intrinsic connections among multiple tasks are used, and multitask machine learning is applied to develop a multitask Gaussian graphical model. The psychological networks of the population and subpopulations are estimated based on psychological questionnaire data. This study is the first to apply a psychological network to such a large-scale college student psychological analysis, and we obtain some interesting results. The model presented here is a dynamic model based on complex networks which predicts individual behavior and provides insight into the intrinsic links among various symptoms. (shrink)

The accurate recognition of speech is beneficial to the fields of machine translation and intelligent human–computer interaction. After briefly introducing speech recognition algorithms, this study proposed to recognize speech with a recurrent neural network (RNN) and adopted the connectionist temporal classification (CTC) algorithm to align input speech sequences and output text sequences forcibly. Simulation experiments compared the RNN-CTC algorithm with the Gaussian mixture model–hidden Markov model and convolutional neural network-CTC algorithms. The results demonstrated that (...) the more training samples the speech recognition algorithm had, the higher the recognition accuracy of the trained algorithm was, but the training time consumption increased gradually; the more samples a trained speech recognition algorithm had to test, the lower the recognition accuracy and the longer the testing time. The proposed RNN-CTC speech recognition algorithm always had the highest accuracy and the lowest training and testing time among the three algorithms when the number of training and testing samples was the same. (shrink)

We propose a novel probabilistic image selection method for the Web image gathering system we proposed before. It employed two-step processing: (1) Gather HTML files of Web pages related to given keywords, analyze them and fetch only Web images expected to be highly related to the keywords. (2) Select only relevant images from the gathered images based on the image-feature-based clustering. In this paper, we propose building a generative model based on the Gaussian mixture model (...) to represent the distribution of image features of images related to the given keywords, and applying it to select images instead of the processing (2). We call the new system ``Probabilistic Image Collector''. We show the effectiveness of our proposed system by the experimental results. (shrink)

Children show a remarkable degree of consistency in learning some words earlier than others. What patterns of word usage predict variations among words in age of acquisition? We use distributional analysis of a naturalistic corpus of child‐directed speech to create quantitative features representing natural variability in word contexts. We evaluate two sets of features: One set is generated from the distribution of words into frames defined by the two adjacent words. These features primarily encode syntactic aspects of word (...) usage. The other set is generated from non‐adjacent co‐occurrences between words. These features encode complementary thematic aspects of word usage. Regression models using these distributional features to predict age of acquisition of 656 early‐acquired English words indicate that both types of features improve predictions over simpler models based on frequency and appearance in salient or simple utterance contexts. Syntactic features were stronger predictors of children's production than comprehension, whereas thematic features were stronger predictors of comprehension. Overall, earlier acquisition was predicted by features representing frames that select for nouns and verbs, and by thematic content related to food and face‐to‐face play topics; later acquisition was predicted by features representing frames that select for pronouns and question words, and by content related to narratives and object play. (shrink)

This article reports results from a program that produces high‐quality animation of facial expressions and head movements as automatically as possible in conjunction with meaning‐based speech synthesis, including spoken intonation. The goal of the research is as much to test and define our theories of the formal semantics for such gestures, as to produce convincing animation. Towards this end, we have produced a high‐level programming language for three‐dimensional (3‐D) animation of facial expressions. We have been concerned primarily (...) with expressions conveying information correlated with the intonation of the voice: This includes the differences of timing, pitch, and emphasis that are related to such semantic distinctions of discourse as “focus,”“topic,” and “comment,”“theme” and “rheme,” or “given” and “new” information. We are also interested in the relation of affect or emotion to facial expression. Until now, systems have not embodied such rule‐governed translation from spoken utterance meaning to facial expressions. Our system embodies rules that describe and coordinate these relations: intonation/information, intonation/affect, and facial expressions/affect. A meaning representation includes discourse information: What is contrastive/background information in the given context, and what is the “topic” or “theme” of the discourse? The system maps the meaning representation into how accents and their placement are chosen, how they are conveyed over facial expression, and how speech and facial expressions are coordinated. This determines a sequence of functional groups: lip shapes, conversational signals, punctuators, regulators, and manipulators. Our algorithms then impose synchrony, create coarticulation effects, and determine affectual signals, eye and head movements. The lowest level representation is the Facial Action Coding System (FACS), which makes the generation system portable to other facial models. (shrink)

This paper argues that neurolinguistics has the potential to yield insights that can feed back into corpus-based Cognitive Linguistics. It starts by discussing how far the cognitive realism of probabilistic statements derived from corpus data currently goes. Against this background, it argues that the cognitive realism of usage-based models could be further enhanced through deeper engagement with neurolinguistics, but also highlights a number of common misconceptions about what neurolinguistics can and cannot do for linguistic theorizing.

This article presents a multimodal video corpus with the principal aim to model and predict the effects of aging in Mild Cognitive Impairment situation on pragmatic and communicative skills. We take as observable variables the verbal pragmatic markers and non-verbal pragmatic markers. This approach, at the interface of the psycholinguistics, cognitive sciences and rehabilitation medicine (speech-language pathology and therapy) is part of a longitudinal research process in an ecological situation (interviews conducted by close intimate of the elderly).In the (...) first part of the article we present the linguistic, cognitive and social characteristics of aging in its continuum up to mild cognitive impairment and pathological disorders such as Alzheimer’s disease. In the second part, we develop a multimodal approach, in particular to inform and enrich speech and language therapy knowledge. Finally, we present our experimental design and preliminary results on two female participants over 75 years of age with mild cognitive impairment.Our general findings indicate that with aging, verbal pragmatic markers acquire an interactive function that allows people with Mild Cognitive Impairment to maintain intersubjective relationships with their interlocutor. In addition, at the non-verbal level, gestural manifestations are increasingly mobilized over time with a preference for non-verbal pragmatic markers with a referential function and an interactive function. One such non-verbal manifestation compensates for naming deficits, planning difficulties, discursive hitches; while another optimizes and maintains the interaction with the interlocutor.Clinicians have a duty to develop their professional practice through an evidence-based clinical approach whose main objective is to reconcile clinical practice with the best evidence from research (Dollaghan 2007). In the case of speech-language pathology, clinicians consider themselves very limited in this approach (Lof 2011; McCurtin 2011), especially for patients with Mild Cognitive Impairment (Mungas et al. 2010; Hopper 2013; Morello 2017) and more specifically when it comes to assessing or supporting language functions (Cummings 2014).The studies focusing on Mild Cognitive Impairment require longitudinal corpora i) to understand the naturally occurring evolutions in subjects, ii) the implication of the cognitive reserve in each individual, and iii) to take advantage of these parameters as evidence for research and earlier rehabilitation. We aim to show the benefits of linguistic and interactional scientific investigation methods through fragile aging, for health professionals and everyday caregivers. (shrink)

Classification in statistics is usually used to solve the problems of identifying to which set of categories, such as subpopulations, new observation belongs, based on a training set of data containing information whose category membership is known. The article aims to use the Gaussian Mixture Model to model the daily closing price index over the period of 1/1/2013 to 16/8/2020 in the Kingdom of Saudi Arabia. The daily closing price index over the period declined, which might be (...) the effect of corona virus, and the mean of the study period is about 7866.965. The closing price is the last regular deal that took place during the continuous trading period. If there are no transactions on the stock during the day, the closing price is the previous day’s closing price. The closing auction period comes after the continuous trading period, during which investors can enter by buying and selling the stocks at this period. The experimental results show that the best mixture model is E with three components according to the BIC criterion. The expectation-maximization algorithm converged in 2 repetitions. The data source is from Tadawul KSA. (shrink)

The Fully Bayesian Significance Test (FBST) is a coherent Bayesian significance test for sharp hypotheses. This paper proposes the FBST as a model selection tool for general mixture models, and compares its performance with Mclust, a model-based clustering software. The FBST robust performance strongly encourages further developments and investigations.

Quotatives have been studied extensively in the language of teenagers in recent years as they present distinctive features of their own that make them different in part from those used by adults in mainstream English and Spanish. However, zero quoting has not received all the attention it certainly deserves as it has not been fully probed in terms of its discourse and pragmatic functions. This corpus-based study is focused on the strategies used by British and Spanish teenagers to (...) introduce constructed dialogue without the use of a particular marked and an explicit quotative form. The results indicate that the absence of quotatives is not casual; there are some linguistic contexts that favor the existence of this type of quotatives, namely non-lexicalized and sound words, and the dramatic recreation of situations where the speaker perform new voices. The lack of quotatives also serves to involve the interlocutor more directly and in a higher degree in what is being told, and gives more self-assurance to the narrator. These tendencies apply equally to English and Spanish. The study of null/zero quotatives should go then beyond the simple reporting of an absence of a verbal mark, and should address pragmatic and interpersonal factors closely associated with the context where they occur and with the speaker’s attitude and communicative intention and purpose. (shrink)

This study proposes an operational approach to a metaphorical framing analysis using large-scale data. We conducted a case analysis of how war metaphors are framed to address various societal issues in a corpus of public speeches by Hong Kong government officials. By investigating patterns of lexical choices under the source domain of WAR and the underlying reasons for the source-target domain mappings (i.e. Mapping Principles), we found that the target domain of social issues in Hong Kong is primarily conceptualized (...) in terms of a combat frame, and governmental issues are primarily talked about in terms of a protection frame, both of which are positively evaluated. Additionally, economic issues are primarily addressed in terms of a strategy frame, which is both positively and negatively evaluated. We show that analyzing the Mapping Principles of these conceptual metaphors captures the “selection” process of framing at the dimensions of frame frequency and frame sentiment, allowing for a principled way to propose a metaphorical framing analysis in corpora-based studies. The proposed approach enriches Critical Discourse Analysis studies of metaphorical framing and bridges the link between metaphor analysis at a conceptual level and framing analysis at a communication level. (shrink)

Name der Zeitschrift: Cognitive Linguistics Jahrgang: 27 Heft: 1 Seiten: 1-33.

When people speak, they often insinuate their intent indirectly rather than stating it as a bald proposition. Examples include sexual come-ons, veiled threats, polite requests, and concealed bribes. We propose a three-part theory of indirect speech, based on the idea that human communication involves a mixture of cooperation and conflict. First, indirect requests allow for plausible deniability, in which a cooperative listener can accept the request, but an uncooperative one cannot react adversarially to it. This intuition is (...) sup- ported by a game-theoretic model that predicts the costs and benefits to a speaker of direct and indirect requests. Second, language has two functions: to convey information and to negotiate the type of relationship holding between speaker and hearer (in particu- lar, dominance, communality, or reciprocity). The emotional costs of a mismatch in the assumed relationship type can create a need for plausible deniability and, thereby, select for indirectness even when there are no tangible costs. Third, people perceive language as a digital medium, which allows a sentence to generate common knowledge, to propagate a message with high fidelity, and to serve as a reference point in coordination games. This feature makes an indirect request qualitatively different from a direct one even when the speaker and listener can infer each other’s intentions with high confidence. (shrink)

Growth mixture models are regularly applied in the behavioral and social sciences to identify unknown heterogeneous subpopulations that follow distinct developmental trajectories. Marcoulides and Trinchera recently proposed a mixture modeling approach that examines the presence of multiple latent classes by algorithmically grouping or clustering individuals who follow the same estimated growth trajectory based on an evaluation of individual case residuals. The purpose of this article was to conduct a simulation study that examines the performance of this (...) new approach for determining the number of classes in growth mixture models. The performance of the approach to correctly identify the number of classes is examined under a variety of longitudinal data design conditions. The findings demonstrated that the new approach was a very dependable indicator of classes across all the design conditions considered. (shrink)

To prevent domains from cyber-piracy, the WIPO offers private and confidential procedures tasked to address the legitimate use of a domain name. WIPO domain name arbitration consists of an alternative dispute resolution process in which one or more panelists make a binding decision over the legitimacy of a domain. This article investigates the structure of the discourse of this professional genre. Following Maley, this study focuses, first, on spotting the generic moves of WIPO domain name arbitration decisions. Second, the analysis (...) unveils patterns shaping WIPO domain name arbitration decisions, hence exploring how discursive features work within this specialized discourse. Third, it examines whether corpus data reveal that this type of professional discourse has entered into a process of standardization. The study is based on Bhatia’s multi-perspective four-space model of discourse. This analytical framework emphasizes a multidisciplinary and multidimensional perspective which highlights that discourse is indistinguishable from constructing reality. (shrink)

The present paper aims to broaden the current understanding of students’ misconception of scientific terminology by identifying the gaps between Arabic and English scientific terminologies and between everyday language and scientific language. The paper compares the polysemy, prototypes, and motivating factors of English energy with those of Arabic طَاقَة (ṭāqa), with more focus on students’ prior knowledge. The study employs Lakoff’s (1987) idealized cognitive models and Rosch’s (1975) prototype theory to reveal the radial members of both categories, i.e., energy (...) and طَاقَة (ṭāqa), and to explain the kinds of cognitive mechanisms that motivate the extension as well as understanding of the meanings of these terms. To this end, the study uses several English and Arabic dictionaries, lexical databases and corpora. This is to explore all the meanings, prototypes and motivating factors of the terms under investigation. The results show that the terms energy and طَاقَة (ṭāqa) overlap in prototypical meanings and motivating factors but differ in less prototypical and peripheral meanings. English and Arabic learners may then face similar issues in learning scientific concepts due to the difference between their pre-existing knowledge and scientific language. (shrink)

Compared to constraint-based causal discovery, causal discovery based on functional causal models is able to identify the whole causal model under appropriate assumptions [Shimizu et al. 2006; Hoyer et al. 2009; Zhang and Hyvärinen 2009b]. Functional causal models represent the effect as a function of the direct causes together with an independent noise term. Examples include the linear non-Gaussian acyclic model, nonlinear additive noise model, and post-nonlinear model. Currently, there are two ways to estimate the (...) parameters in the models: dependence minimization and maximum likelihood. In this article, we show that for any acyclic functional causal model, minimizing the mutual information between the hypothetical cause and the noise term is equivalent to maximizing the data likelihood with a flexible model for the distribution of the noise term. We then focus on estimation of the PNL causal model and propose to estimate it with the warped Gaussian process with the noise modeled by the mixture of Gaussians. As a Bayesian nonparametric approach, it outperforms the previous one based on mutual information minimization with nonlinear functions represented by multilayer perceptrons; we also show that unlike the ordinary regression, estimation results of the PNL causal model are sensitive to the assumption on the noise distribution. Experimental results on both synthetic and real data support our theoretical claims. (shrink)

We trained a computational model (the Chunk-Based Learner; CBL) on a longitudinal corpus of child–caregiver interactions in English to test whether one proposed statistical learning mechanism—backward transitional probability—is able to predict children's speech productions with stable accuracy throughout the first few years of development. We predicted that the model less accurately reconstructs children's speech productions as they grow older because children gradually begin to generate speech using abstracted forms rather than specific “chunks” from their (...) class='Hi'>speech environment. To test this idea, we trained the model on both recently encountered and cumulative speech input from a longitudinal child language corpus. We then assessed whether the model could accurately reconstruct children's speech. Controlling for utterance length and the presence of duplicate chunks, we found no evidence that the CBL becomes less accurate in its ability to reconstruct children's speech with age. (shrink)

Gene clustering is a useful exploratory technique to group together genes with similar expression levels under distinct cell cycle phases or distinct conditions. It helps the biologist to identify potentially meaningful relationships between genes. In this study, we propose a clustering method based on multivariate normal mixture models, where the number of clusters is predicted via sequential hypothesis tests: at each step, the method considers a mixture model of m components (m = 2 in the first (...) step) and tests if in fact it should be m - 1. If the hypothesis is rejected, m is increased and a new test is carried out. The method continues (increasing m) until the hypothesis is accepted. The theoretical core of the method is the full Bayesian significance test, an intuitive Bayesian approach, which needs no model complexity penalization nor positive probabilities for sharp hypotheses. Numerical experiments were based on a cDNA microarray dataset consisting of expression levels of 205 genes belonging to four functional categories, for 10 distinct strains of Saccharomyces cerevisiae. To analyze the method’s sensitivity to data dimension, we performed principal components analysis on the original dataset and predicted the number of classes using 2 to 10 principal components. Compared to Mclust (model-based clustering), our method shows more consistent results. (shrink)

The article describes two approaches to modeling the concept system of science – the thematic and paradigm ones. The re­search represents a case study of the two corpuses of abstracts: abstracts of projects supported by the Department of Humanities and Social Sciences of the Russian Federal Property Fund in lin­guistics, as well abstracts of articles by authors (and their co-au­thors) who have received multiple support from this foundation. Thematic modeling was carried out within the frameworks of two approaches: сcorpus (...) class='Hi'>based approach (modeling the system of concepts as a holistic entity of term fields network), and single text approach (singling out compositions of term fields steadily present in the texts of projects which can be treated as separate branches of linguistics). The method of semantic graph modeling realized in the “Semograph” Information system (corpus based approach) and the K-means clustering method (single text based approach) were applied. Arrays of keywords (those that occurred in the abstracts of projects supported by the Foundation) grouped into term fields served as operational units. Paradigmatic model­ing was based on the analysis of network interaction of research­ers (created on the basis of the information on joint publications). After the hypergraph of researchers (2,108 state points) had been created, it was divided into subgraphs (network communities) by means of the modularity method (analogue of cluster analysis). Each cluster can be considered as a model of a scientific commu­nity that actualize a certain scientific paradigm; a set of clusters represents a model of the concept system of a certain subject domain from the point of view of representation and interrela­tion of several paradigms. The synthesis of thematic and paradig­matic models appears to be the direction for future research that involves modeling the concept system of science. The considered models can be applied as an effective means of monitoring, fore­casting and managing scientific research, i.e. as an instrument of state and/or department policy. (shrink)

This paper presents selected findings from a historical analysis of change in the discursive construction of social identity in UK education policy discourse from 1972–2005. My chief argument is that through its linguistic forms of self-identification the government construes educational roles, relations and responsibilities not only for itself, but also for other educational actors and wider society. More specifically, I argue that New Labour's distinctive mode of self-representation is an important element in its hegemonic project, textually manufacturing consent over its (...) policy decisions, and helping to articulate its self-styled ‘enabling’ model of governance. As evidence for these claims I discuss two prominent trends in New Labour's education policy rhetoric, which I characterise as ‘personalisation’ and ‘managerialisation’. Respectively, these relate to the discursive representation of social identity and social action. (shrink)

With its connotations of superior moral integrity, exceptional leadership qualities and expertise in the science of government, the modern ideal of statesmanship is most commonly traced back to the ancient Greek concept of πολιτικός and the work of Plato and Aristotle in particular. Through an analysis of a large corpus of modern English translations of political works, built as part of the AHRC Genealogies of Knowledge project, this case-study aims to explore patterns that are specific to this translated discourse, (...) with a view to understanding the crucial role played by translators in shaping its development and reception in society. It ultimately seeks to argue that the model of statesmanship presented in translations from ancient Greek is just as much a product of the receiving culture as it is inherited from the classical world. (shrink)

Words in Semitic texts often consist of a concatenation of word segments, each corresponding to a Part-of-Speech (POS) category. Semitic words may be ambiguous with regard to their segmentation as well as to the POS tags assigned to each segment. When designing POS taggers for Semitic languages, a major architectural decision concerns the choice of the atomic input tokens (terminal symbols). If the tokenization is at the word level the output tags must be complex, and represent both the segmentation (...) of the word and the POS tag assigned to each word segment. If the tokenization is at the segment level, the input itself must encode the different alternative segmentations of the words, while the output consists of standard POS tags. Comparing these two alternatives is not trivial, as the choice between them may have global effects on the grammatical model. Moreover, intermediate levels of tokenization between these two extremes are conceivable, and, as we will aim to show, beneficial. To the best of our knowledge, the problem of tokenization for POS tagging of Semitic languages has not been addressed before in full generality. In this paper, we study this problem for the purpose of POS tagging of Modern Hebrew texts. After extensive error analysis of the two simple tokenization models, we propose a novel, linguistically-motivated, intermediate tokenization model that gives better performance for Hebrew over the two initial architectures. Our study is based on the well-known Hidden Markov Models (HMMs). We start out from a manually devised morphological analyzer and a very small annotated corpus, and describe how to adapt an HMM-based POS tagger for both tokenization architectures. We present an effective technique for smoothing the lexical probabilities using an untagged corpus, and a novel transform for casting the segment-level tagger in terms of a standard, word-level, HMM implementation. The results obtained using our model are on par with the best published results on Modern Standard Arabic, despite the much smaller annotated corpus available for Modern Hebrew.. (shrink)

Concern about air-quality in urban areas has led to the implementation of Low Emission Zones as one of many other initiatives to control it. Recently in Spain, the enactment of a law made this mandatory for cities with a population larger than 50k inhabitants. The delimitation of these areas is not without controversy because of possible negative economic and social impacts. Therefore, clear assessments of how these initiatives decrease pollutant concentrations are to be provided. Madrid Central is a major initiative (...) of Madrid city council for reducing motor traffic and the associated air pollution in the city centre. This Low Emission Zone starts at the end of 2018, but the first fully-operational period corresponds to the second quarter of 2019. In this work, a methodology based on the Gaussian Process to analyse the evolution of Nitrogen Dioxide inside Madrid Central is undertaken. A Gaussian Process is a stochastic process suitable for interpretable model selection and predictions. Due to its probabilistic nature it provides error estimation at predictions. After the activation of Madrid Central, a relevant reduction of Nitrogen Dioxide has been observed. However, the role of the meteorology during this period must be ascertained to correctly evaluate the role of the activation of the Low Emission Zone against a prone weather. In this work, a model based on the Gaussian Process is trained with meteorological information to predict the concentration of Nitrogen Dioxide at Madrid Central, $[NO_{2}]$. This probabilistic description allows extracting statistical information on the reduction affected by the meteorological scenario and separately by the Madrid Central activation. (shrink)

The aim of this research is to analyze the political discourse and the language they used in public addresses provided by two former presidents of the United States, specifically, President Barak Obama and President Donald Trump. This research reflects on the material that has been collected for several months which aimed to contribute on the analysis of the corpus, distinctions and similarities, as well as their attitudes towards the public opinion. The objectives of this study are mainly empirical and (...) theoretical. Considering the empirical objectives, this study seeks to explore, identify and categorize the most used words in a political speech according to the categories, form, function and meaning, whereas, the theoretical objectives of this study are to provide a description of the language that is used in major events from well-known politicians when aiming to capture people’s attention. Their rhetorical habits were examined, in every delivery of Mr. Obama’s political speech; his speeches were carefully and well-articulated throughout his 8 year political service in the oval office, portraying him as a kind person. On the other hand, Mr. Trump’s, as a non-politician, more as a businessman, political speeches throughout his campaign and 4 years in the office were very often seen as aggressive, unforeseen, and nonetheless straight to the point. This study covers the linguistic level analysis as well as semantic and pragmatic levels. Initially, the selected corpora were analyzed using the Ant Conc software regarding frequency of usage and then move into detailed analysis. (shrink)

The linguistic complexity of words has largely been studied on the behavioral level and in experimental settings. Only little is known about the neural processes underlying it in uninstructed, spontaneous conversations. We built up a multimodal neurolinguistic corpus composed of synchronized audio, video, and electrocorticographic (ECoG) recordings from the fronto-temporo-parietal cortex to address this phenomenon based on uninstructed, spontaneous speech production. We performed extensive linguistic annotations of the language material and calculated word complexity using several numeric parameters. (...) We orthogonalized the parameters with the help of a linear regression model. Then, we correlated the spectral components of neural activity with the individual linguistic parameters and with the residuals of the linear regression model, and compared the results. The proportional relation between the number of consonants and vowels, which was the most informative parameter with regard to the neural representation of word complexity, showed effects in two areas: the frontal one was at the junction of the premotor cortex, the prefrontal cortex, and Brodmann area 44. The postcentral one lay directly above the lateral sulcus and comprised the ventral central sulcus, the parietal operculum and the adjacent inferior parietal cortex. Beyond the physiological findings summarized here, our methods may be useful for those interested in ways of studying neural effects related to natural language production and in surmounting the intrinsic problem of collinearity between multiple features of spontaneously spoken material. (shrink)

Although the language we encounter is typically embedded in rich discourse contexts, many existing models of processing focus largely on phenomena that occur sentence-internally. Similarly, most work on children's language learning does not consider how information can accumulate as a discourse progresses. Research in pragmatics, however, points to ways in which each subsequent utterance provides new opportunities for listeners to infer speaker meaning. Such inferences allow the listener to build up a representation of the speakers' intended topic and more (...) generally to identify relationships, structures, and messages that extend across multiple utterances. We address this issue by analyzing a video corpus of child–caregiver interactions. We use topic continuity as an index of discourse structure, examining how caregivers introduce and discuss objects across utterances. For the analysis, utterances are grouped into topical discourse sequences using three annotation strategies: raw annotations of speakers' referents, the output of a model that groups utterances based on those annotations, and the judgments of human coders. We analyze how the lexical, syntactic, and social properties of caregiver–child interaction change over the course of a sequence of topically related utterances. Our findings suggest that many cues used to signal topicality in adult discourse are also available in child-directed speech. (shrink)

This paper presents a kernel density estimation method by means of real-coded crossovers. Functions of real-coded crossover operators are composed of probabilistic density estimation from parental populations and sampling from estimated models. Real-coded Genetic Algorithm (RCGA) does not explicitly estimate probabilistic distributions, however, probabilistic model estimation is implicitly included in algorithms of real-coded crossovers. Based on this understanding, we exploit the implicit estimation of probabilistic distribution of crossovers as a kernel density estimator. We also propose an application of (...) crossover kernels to Expectation-Maximization estimation (EM) of Gaussian mixtures. (shrink)

Defamation law is a long-standing research focus. Previous studies on defamation law have pointed out the importance of balancing two fundamental issues in law, namely, protection of reputation and freedom of speech. The present corpus-based legal study, using ConcGram 1.0 as the analytical tool, examined the phraseological profile of reported cases on defamation in Hong Kong in order to find out the types of defense and the approach to meaning in the defamation case law in Hong Kong. (...) Regarding defenses to a defamation claim, the results show that fair comment, qualified privilege, and justification are the most prevalent types, that unintentional defamation is not used at all, and that there has been a noticeable shift from fair comment to honest comment. As for the approach to meaning, the ordinary and natural approach is found to be a pivotal means of solving the threshold problem in defamation cases, that is, whether the words involved are defamatory or not. (shrink)

We explore the nature of forgetting in a corpus of 125,000 students learning Spanish using the Rosetta Stone® foreign-language instruction software across 48 lessons. Students are tested on a lesson after its initial study and are then retested after a variable time lag. We observe forgetting consistent with power function decay at a rate that varies across lessons but not across students. We find that lessons which are better learned initially are forgotten more slowly, a correlation which likely reflects (...) a latent cause such as the quality or difficulty of the lesson. We obtain improved predictive accuracy of the forgetting model by augmenting it with features that encode characteristics of a student's initial study of the lesson and the activities the student engaged in between the initial and delayed tests. The augmented model can predict 23.9% of the variance in an individual's score on the delayed test. We analyze which features best explain individual performance. (shrink)

Professionals in various disciplines adopt significantly different lexicons to report their discoveries and arguments. Scientists discover, philosophers argue, whereas legal practitioners apply and consider. Reporting, as a ubiquitous linguistic phenomenon, has its disciplinary characteristics. In court judgments, it reflects the way judges identify the evidence of different documents or other courts. In the self-built court judgment corpus, the paper focuses on the way that judicial arguments are constructed through reporting verbs. On the basis of the analysis of the representation (...) and distribution of reporting verbs in court judgments, the study identifies the reporting verbs with high frequencies in court judgments and compares these reporting verbs with those in the comparable reference corpus, written sampler of British National Corpus, which works as a reference corpus. It is found that the tokens of reporting verbs in court judgments are slightly less than those in general texts; the distribution pattern of the speech act verbs and mental verbs in the self-built court judgment corpus is similar to the pattern of two kinds of reporting verbs in the reference corpus; judicial speech act verbs are employed to express the authority of the statements, whereas the judicial mental verbs illustrate the legal reasoning process with individual agents; and there is a significant difference in terms of the reporting verbs’ frequencies within the court judgments and general texts. The results show that the reporting verbs in court judgments have their uniqueness, which may cast light on both judicial and pedagogical practices. (shrink)

This paper presents the results of an empirical study following up on Mizrahi (2021). Using the same methods of text mining and corpus analysis used by Mizrahi (2021), we test empirically a philosophical account of scientific progress that Mizrahi (2021) left out of his empirical study, namely, the so-called functional-internalist account of scientific progress according to which the aim or goal or scientific research is to solve problems. In general, our results do not lend much empirical evidence in support (...) of the problem-solving model of scientific progress over the other philosophical accounts of scientific progress (namely, the epistemic, noetic, and semantic accounts of scientific progress) tested in Mizrahi (2021) and in this follow-up study. Of all the subjects in the JSTOR database we have tested in this study, however, Mathematics is an interesting exception as far as the problem-solving model of scientific progress is concerned. For, in Mathematics alone, we have found that there is significantly more talk of the aims and/or goals of research in terms of solutions than in terms of truth, knowledge, or understanding. (shrink)

This paper investigates the effects of 10 factors on the choice between alternative ba sentences and SVO sentences in Mandarin Chinese. These factors are givenness, definiteness, animacy and pronominality of NP2s, NP2 length, VP length, verb sense, syntactic parallelism, dependency distance, and surprisal. Using corpus data and mixed-effects logistic regression modeling, we find that on the one hand, givenness, syntactic parallelism, and the log-transformed ratio of NP2 length and VP length are significant predictors of the choice between ba sentences (...) and SVO sentences. A new NP2, a large length ratio and a parallel construction predict an SVO sentence rather than a ba sentence. On the other hand, dependency distance and surprisal estimated by the trigram model are effective in predicting the choice between naturally occurring ba/svo sentences and their alternatives. Naturally occurring sentences are more likely to have shorter dependency distances and smaller surprisal values than the converted sentences. The effects of these five factors on syntactic choice are congruent with results of previous studies, which suggests that some determinants of syntactic choice are shared among languages. (shrink)

This paper develops a construction-based dialectometry capable of identifying previously unknown constructions and measuring the degree to which a given construction is subject to regional variation. The central idea is to learn a grammar of constructions using construction grammar induction and then to use these constructions as features for dialectometry. This offers a method for measuring the aggregate similarity between regional CxGs without limiting in advance the set of constructions subject to variation. The learned CxG is evaluated on how (...) well it describes held-out test corpora while dialectometry is evaluated on how well it can model regional varieties of English. The method is tested using two distinct datasets: First, the International Corpus of English representing eight outer circle varieties; Second, a web-crawled corpus representing five inner circle varieties. Results show that the method produces a grammar with stable quality across sub-sets of a single corpus that is capable of distinguishing between regional varieties of English with a high degree of accuracy, thus supporting dialectometric methods for measuring the similarity between varieties of English and measuring the degree to which each construction is subject to regional variation. This is important for cognitive sociolinguistics because it operationalizes the idea that competition between constructions is organized at the functional level so that dialectometry needs to represent as much of the available functional space as possible. (shrink)