Theories of language production propose that utterances are constructed by a mechanism that separates linguistic content from linguistic structure, Linguistic content is retrieved from the mental lexicon, and is then inserted into slots in linguistic structures or frames. Support for this kind of model at the phonological level comes from patterns of phonological speech errors. W present an alternative account of these patterns using a connectionist or parallel distributed proceesing (PDP) model that learns to produce sequences of phonological (...) features. The model's errors exhibit some of the properties of human speech errors, specifically, properties that have been attributed to the action of phonological rules, frames, or other structural generalizations. (shrink)

Inner speech is commonly understood as the conscious experience of a voice within the mind. One recurrent theme in the scientific literature is that the phenomenon involves a representation of overt speech, for example, a representation of phonetic properties that result from a copy of speech instructions that were ultimately suppressed. I propose a larger picture that involves some embodied objects and their misperception. I call it “the Body Error Theory,” or BET for short. BET is a (...) form of illusionism, but the particular version I favor is a cross-modal illusion. Newly described here, my hypothesis is that the experience of inner speech arises from a mix of interoception and audition. Specifically, there is the detection of slight but well-confirmed activities in the speech musculature that occur during inner speech, which helps to transform representations of normal but quiet nonverbal sounds that inevitably occur during inner speech, from breathing to background noise, into a mistaken perception of inner speech. Simply put, activities in the speech musculature mix with sounds to create the appearance of speech sounds, which thus explains the “voice within the mind.” I also show how BET’s cross-modal system fits with standard information processing accounts for speech monitoring and how it accommodates the central insights of leading theories of inner speech. In addition, I show how BET is supported by data from experience-sampling surveys and how it can be empirically tested against its rivals. (shrink)

Constraints on the types of speech errors observed can be accounted for by a frame/content distinction, but connectionist modeling shows that they do not require this distinction. The constraints may arise instead from the statistical properties of our language, in particular, the sequential biases observed in the vocabulary. Nevertheless, there might still be a role for the frame/content distinction in syntactic planning.

Free speech fallacies are errors of meta-argument. One commits a free speech fallacy when one argues that since there are apparent restrictions on one’s rights of free expression, procedural rules of critical exchange have been broken, and consequently, one’s preferred view is dialectically better off than it may otherwise seem. Free speech fallacies are meta-argumentative, since they occur at the level of assessing the dialectical situation in terms of norms of argument and in terms of meta-evidential (...) principles of interpreting how and why people follow (or fail to follow) argumentative rules. Our plan here is to begin with a brief explanation of meta-argument and meta-argumentative fallacy. We will then turn to the variety of forms of the free speech fallacy, which we will explain as meta-argumentatively erroneous. (shrink)

Are there moral facts? According to moral nihilism, the answer is no. Some moral nihilists are moral error theorists, who think that moral judgements purport to refer to moral facts, but since there are no moral facts, moral judgements are uniformly false or untrue. Terence Cuneo has recently raised an original and potentially very serious objection to moral error theory. According to Cuneo’s ‘normative theory of speech’, normative facts, some of which are moral facts, are crucially involved in explanations (...) of how it is that we are able to perform illocutionary speech acts, such as asserting, promising, and commanding. Many versions of moral error theory reject not only moral facts, but also normative facts of the kind Cuneo takes to be among the prerequisites of our abilities to perform illocutionary speech acts. If Cuneo’s argument is successful, then, moral error theory has the unsettling implication that we do not speak, and possibly that we cannot speak. I shall argue, however, that the argument ultimately fails, chiefly because its core premise fails to establish that illocutionary speech acts are normative in the first place. (shrink)

This chapter focuses on one of the common fallacies in Western philosophy: free speech fallacy (FS). The FS consists in thinking one's political right to freedom of expression includes protection from criticism. Those who commit this fallacy allege that critical scrutiny is either tantamount to censorship or equivalent to the imposition of one's views on others. The error in the fallacy is that the freedom of expression includes critical expressions. The trouble with the argument is that freedom of expression (...) does not mean freedom from criticism. The first distortion concerns the logical consequences of criticism. The second distortion confuses logical implications of criticism with the criticism itself. The FS thus takes the personal implications, necessary though they may be, as the defining features of criticism, when they are merely incidental. (shrink)

Linguistic intuitions are a central source of evidence across a variety of linguistic domains. They have also long been a source of controversy. This chapter aims to illuminate the etiology and evidential status of at least some linguistic intuitions by relating them to error signals of the sort posited by accounts of on-line monitoring of speech production and comprehension. The suggestion is framed as a novel reply to Michael Devitt’s claim that linguistic intuitions are theory-laden “central systems” responses, rather (...) than endorsed outputs of a modularized language faculty (the “Voice of Competence”). Along the way, it is argued that linguistic intuitions may not constitute a natural kind with a common etiology; and that, for a range of cases, the process by which intuitions used in linguistics are generated amounts to little more than comprehension. (shrink)

This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work. This work is in the public domain (...) in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. (shrink)

Preparing words in speech production is normally a fast and accurate process. We generate them two or three per second in fluent conversation; and overtly naming a clear picture of an object can easily be initiated within 600 msec after picture onset. The underlying process, however, is exceedingly complex. The theory reviewed in this target article analyzes this process as staged and feedforward. After a first stage of conceptual preparation, word generation proceeds through lexical selection, morphological and phonological encoding, (...) phonetic encoding, and articulation itself. In addition, the speaker exerts some degree of output control, by monitoring of self-produced internal and overt speech. The core of the theory, ranging from lexical selection to the initiation of phonetic encoding, is captured in a computational model, called WEAVER++. Both the theory and the computational model have been developed in interaction with reaction time experiments, particularly in picture naming or related word production paradigms, with the aim of accounting for the real-time processing in normal word production. A comprehensive review of theory, model, and experiments is presented. The model can handle some of the main observations in the domain of speech errors (the major empirical domain for most other theories of lexical access), and the theory opens new ways of approaching the cerebral organization of speech production by way of high-temporal-resolution imaging. (shrink)

The ability to explain the occurrence of errors in children's speech is an essential component of successful theories of language acquisition. The present study tested some generativist and constructivist predictions about error on the questions produced by ten English-learning children between 2 and 5 years of age. The analyses demonstrated that, as predicted by some generativist theories [e.g. Santelmann, L., Berk, S., Austin, J., Somashekar, S. & Lust. B. (2002). Continuity and development in the acquisition of inversion in (...) yes/no questions: dissociating movement and inflection, Journal of Child Language, 29, 813-842], questions with auxiliary DO attracted higher error rates than those with modal auxiliaries. However, in wh-questions, questions with modals and DO attracted equally high error rates, and these findings could not be explained in terms of problems forming questions with why or negated auxiliaries. It was concluded that the data might be better explained in terms of a constructivist account that suggests that entrenched item-based constructions may be protected from error in children's speech, and that errors occur when children resort to other operations to produce questions [e.g. Dabrowska, E. (2000). From formula to schema: the acquisition of English questions. Cognitive Liguistics, 11, 83-102; Rowland, C. F. & Pine, J. M. (2000). Subject-auxiliary inversion errors and wh-question acquisition: What children do know? Journal of Child Language, 27, 157-181; Tomasello, M. (2003). Constructing a language: A usage-based theory of language acquisition. Cambridge, MA: Harvard University Press]. However, further work on constructivist theory development is required to allow researchers to make predictions about the nature of these operations. (shrink)

Hate speech is commonly defined with reference to the legal category of incitement. Laws targeting incitement typically focus on how the speech is expressed rather than its actual content. This has a number of unintended consequences: first, law tends to capture overt or obvious forms of hate speech and not hate speech that takes the form of ‘reasoned’ argument, but which nevertheless, causes as much, if not more harm. Second, the focus on form rather than content (...) leads to categorization errors. Hate speech taking the form of ‘reasoned argument’ is often legally characterized as either political or academic debate, and so is deemed both permissible and justified in societies where free speech principles exist. In this paper, I argue that it is important to identify instances of hate speech as hate speech, no matter how articulately or reasonably the speech is expressed. The danger in mischaracterizing an instance of hate speech by calling it academic or political debate is that it risks normalizing the views and sentiments that are expressed and accepting those views as an important part of our political and academic discourses. With reference to Habermas’ account of ‘distorted communication’, I propose different criteria for defining and understanding hate speech and suggest that there might be good reasons for interpreting the concept of incitement more broadly, so as to include these different kinds of it. (shrink)

Stawarska considers the ambiguities surrounding the antagonism between the phenomenological and the structuralist traditions by pointing out that the supposed foundation of structuralism, the Course in General Linguistics, was ghostwritten posthumously by two editors who projected a dogmatic doctrine onto Saussure’s lectures, while the authentic materials related to Saussure’s linguistics are teeming with phenomenological references. She then narrows the focus to Merleau-Ponty’s engagement with Saussure’s linguistics and argues that it offers an unusual, if not an uncanny, reading of the Course, (...) in that it identifies a phenomenological dimension within the text, against the grain of the dominant structuralist claim. This phenomenological dimension is corroborated by the authentic sources of Saussure’s linguistics, even though the latter were beyond the philosopher’s own power to know. Merleau-Ponty’s unorthodox reading of the Course as being broadly compatible with the tradition of Husserlian phenomenology has been dismissed as an error and a contresens , but Stawarska proposes that such deviant appropriations of foundational texts are the ones to cherish the most, since they effectively dismantle the received dogmas and official doctrines stuffing the cabinets of canonical philosophy. She argues specifically that Merleau-Ponty’s contested distinction between “a synchronic linguistics of speech ” and “a diachronic linguistics of language ” , which gives primacy to la parole over la langue, and raises the possibility of a systematic study of la parole, contains a more faithful response to Saussure’s own project than the received structuralist view that la langue alone constitutes the proper object of linguistic study. (shrink)

The overall goal of speech perception research is to explain how spoken language is recognized and understood. In the current research framework it is assumed that the key to achieving this overall goal is to solve the lack of invariance problem. But nearly half a century of sustained effort in a variety of theoretical perspectives has failed to solve this problem. Indeed, not only has the problem not been solved, virtually no empirical candidates for solving the problem have been (...) produced. One explanation for this lack of progress is simply that no theory has yet hit upon the correct set of invariant properties. Another explanation is that the goal of solving the lack of invariance problem is itself misguided. The most basic claim of this dissertation is that the latter explanation is correct. ;The lack of progress in explaining speech perception exhibited by the current research framework, I argue, is not, in the first instance, due to the failure of individual theories to solve the lack of invariance problem, but rather to the common background assumption that doing so is in fact the key to explaining speech perception. ;My overall argument in support of this basic claim has three main components: criticism of the empirical results of research in the current framework; criticism of the formulation of theories generated in the current research framework; and the availability of an alternative account of phonetic structure. ;The heart of my argument is an analysis of the character of the theoretical weaknesses of three theories of speech perception: the motor theory of speech perception, the ecological theory of speech perception, and the theory of acoustic invariance. I show that, in each case, the particular theoretical problems result from trying to satisfy the common goal of identifying invariant properties of phonetic percepts. ;I analyze the methodological error embodied in the current approach to explaining speech perception as, in general, one of abstracting the wrong kinds of properties from the detail of the speech event, and, in particular, of abstracting away from dynamic and context-dependent properties. (shrink)

This paper outlines the background development of “intelligent“ technologies such as speech recognition. Despite significant progress in the development of these technologies, they still fall short in many areas, and rapid advances in areas such as dictation are actually stalled. In this paper we have proposed semi-automatic solutions — smart integration of human and intelligent efforts. One such technique involves improvement to the speech recognition editing interface, thereby reducing the perception of errors to the viewer. Other techniques (...) that are described in the paper are batch enrollment, which allows the user to reduce the amount of time required for enrollment, and content spotting, which can be used for applications that have repeated content flow, such as movies or museum tours. The paper also suggests a general concept of distributive training of speech recognition systems that is based on data collection across a network. (shrink)

Stawarska considers the ambiguities surrounding the antagonism between the phenomenological and the structuralist traditions by pointing out that the supposed foundation of structuralism, the Course in General Linguistics, was ghostwritten posthumously by two editors who projected a dogmatic doctrine onto Saussure’s lectures, while the authentic materials related to Saussure’s linguistics are teeming with phenomenological references. She then narrows the focus to Merleau-Ponty’s engagement with Saussure’s linguistics and argues that it offers an unusual, if not an uncanny, reading of the Course, (...) in that it identifies a phenomenological dimension within the text, against the grain of the dominant structuralist claim. This phenomenological dimension is corroborated by the authentic sources of Saussure’s linguistics, even though the latter were beyond the philosopher’s own power to know. Merleau-Ponty’s unorthodox reading of the Course as being broadly compatible with the tradition of Husserlian phenomenology has been dismissed as an error and a contresens, but Stawarska proposes that such deviant appropriations of foundational texts are the ones to cherish the most, since they effectively dismantle the received dogmas and official doctrines stuffing the cabinets of canonical philosophy. She argues specifically that Merleau-Ponty’s contested distinction between “a synchronic linguistics of speech ” and “a diachronic linguistics of language ”, which gives primacy to la parole over la langue, and raises the possibility of a systematic study of la parole, contains a more faithful response to Saussure’s own project than the received structuralist view that la langue alone constitutes the proper object of linguistic study. (shrink)

This paper outlines the background development of “intelligent“ technologies such as speech recognition. Despite significant progress in the development of these technologies, they still fall short in many areas, and rapid advances in areas such as dictation are actually stalled. In this paper we have proposed semi-automatic solutions — smart integration of human and intelligent efforts. One such technique involves improvement to the speech recognition editing interface, thereby reducing the perception of errors to the viewer. Other techniques (...) that are described in the paper are batch enrollment, which allows the user to reduce the amount of time required for enrollment, and content spotting, which can be used for applications that have repeated content flow, such as movies or museum tours. The paper also suggests a general concept of distributive training of speech recognition systems that is based on data collection across a network. (shrink)

Speech recognition technology has played an indispensable role in realizing human-computer intelligent interaction. However, most of the current Chinese speech recognition systems are provided online or offline models with low accuracy and poor performance. To improve the performance of offline Chinese speech recognition, we propose a hybrid acoustic model of deep convolutional neural network, long short-term memory, and deep neural network. This model utilizes DCNN to reduce frequency variation and adds a batch normalization layer after its convolutional (...) layer to ensure the stability of data distribution, and then use LSTM to effectively solve the gradient vanishing problem. Finally, the fully connected structure of DNN is utilized to efficiently map the input features into a separable space, which is helpful for data classification. Therefore, leveraging the strengths of DCNN, LSTM, and DNN by combining them into a unified architecture can effectively improve speech recognition performance. Our model was tested on the open Chinese speech database THCHS-30 released by the Center for Speech and Language Technology of Tsinghua University, and it was concluded that the DLD model with 3 layers of LSTM and 3 layers of DNN had the best performance, reaching 13.49% of words error rate. (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)

In order to improve the efficiency of the English translation, machine translation is gradually and widely used. This study briefly introduces the neural network algorithm for speech recognition. Long short-term memory (LSTM), instead of traditional recurrent neural network (RNN), was used as the encoding algorithm for the encoder, and RNN as the decoding algorithm for the decoder. Then, simulation experiments were carried out on the machine translation algorithm, and it was compared with two other machine translation algorithms. The results (...) showed that the back-propagation (BP) neural network had a lower word error rate and spent less recognition time than artificial recognition in recognizing the speech; the LSTM–RNN algorithm had a lower word error rate than BP–RNN and RNN–RNN algorithms in recognizing the test samples. In the actual speech translation test, as the length of speech increased, the LSTM–RNN algorithm had the least changes in the translation score and word error rate, and it had the highest translation score and the lowest word error rate under the same speech length. (shrink)

Rhythmic structure in speech is characterized by sequences of stressed and unstressed syllables. A large body of literature suggests that speakers of English attempt to achieve rhythmic harmony by evenly distributing stressed syllables throughout prosodic phrases. The question remains as to how speakers plan metrical structure during speech production and whether it is planned independently of phonemes. To examine this, we designed a tongue twister task consisting of disyllabic word pairs with overlapping phonological segments and either matching or (...) non‐matching metrical structure. Results showed that speakers had more difficulty producing metrically regular word pairs, compared to irregular pairs; that is, word pairs with irregular meter had faster productions and fewer speech errors in this production task. This finding of metrical regularity inhibiting production is inconsistent with an abstract metrical structure that is planned independently of phonemes at the point of phonological encoding. (shrink)

I argue that Davidson's anti-skeptical thesis can survive objections made against it by treating skepticism as logically possible, but not epistemically possible. That is, the skeptical hypothesis of massive error conflicts with what we must take ourselves to know if we are to have coherent thought and speech.

Recent Plato scholarship has grown increasingly comfortable with the notion that Plato’s art of writing brings his readers into the dialogue, challenging them to respond to deliberate errors or lacunae in the text. Drawing inspiration from Stanley Fish’s seminal reading of Satan’s speeches in Paradise Lost, this paper considers the narrative of Timaeus as deliberately unreliable, and argues that the actively critical reader is “the missing fourth” with which the dialogue famously begins. By continuing Timaeus with Critias—a dialogue that (...) ends with a missing speech—Plato points to the kind of reader he expects: one who can answer Critias’ question : ὡς μὲν γὰρ οὐκ εὖ τὰ παρὰ σοῦ λεχθέντα εἴρηται, τίς ἂν ἐπιχειρήσειεν ἔμφρων λέγειν. (shrink)

This paper combines domestic and international research results to analyze and study the difference between the attribute features of English phrase speech and noise to enhance the short-time energy, which is used to improve the threshold judgment sensitivity; noise addition to the discrepancy data set is used to enhance the recognition robustness. The backpropagation algorithm is improved to constrain the range of weight variation, avoid oscillation phenomenon, and shorten the training time. In the real English phrase sound recognition system, (...) there are problems such as massive training data and low training efficiency caused by the super large-scale model parameters of the convolutional neural network. To address these problems, the NWBP algorithm is based on the oscillation phenomenon that tends to occur when searching for the minimum error value in the late training period of the network parameters, using the K-MEANS algorithm to obtain the seed nodes that approach the minimal error value, and using the boundary value rule to reduce the range of weight change to reduce the oscillation phenomenon so that the network error converges as soon as possible and improve the training efficiency. Through simulation experiments, the NWBP algorithm improves the degree of fitting and convergence speed in the training of complex convolutional neural networks compared with other algorithms, reduces the redundant computation, and shortens the training time to a certain extent, and the algorithm has the advantage of accelerating the convergence of the network compared with simple networks. The word tree constraint and its efficient storage structure are introduced, which improves the storage efficiency of the word tree constraint and the retrieval efficiency in the English phrase recognition search. (shrink)

I examine what would be necessary to move part-of-speech tagging performance from its current level of about 97.3% token accuracy (56% sentence accuracy) to close to 100% accuracy. I suggest that it must still be possible to greatly increase tagging performance and examine some useful improvements that have recently been made to the Stanford Part-of-Speech Tagger. However, an error analysis of some of the remaining errors suggests that there is limited further mileage to be had either from (...) better machine learning or better features in a discriminative sequence classifier. The prospects for further gains from semisupervised learning also seem quite limited. Rather, I suggest and begin to demonstrate that the largest opportunity for further progress comes from improving the taxonomic basis of the linguistic resources from which taggers are trained. That is, from improved descriptive linguistics. However, I conclude by suggesting that there are also limits to this process. The status of some words may not be able to be adequately captured by assigning them to one of a small number of categories. While conventions can be used in such cases to improve tagging consistency, they lack a strong linguistic basis. (shrink)

Hearing loss in old age, which often goes untreated, has far-reaching consequences. Furthermore, reduction of cognitive abilities and dementia can also occur, which also affects quality of life. The aim of this study was to investigate the hearing performance of seniors without hearing complaints with respect to speech perception in noise and the ability to localize sounds. Results were tested for correlations with age and cognitive performance. The study included 40 subjects aged between 60 and 90 years with not (...) self-reported hearing problems. The subjects were screened for dementia. Audiological tests included pure-tone audiometry and speech perception in two types of background noise which was either co-located or spatially separated from the target speech. Sound localization ability was assessed and hearing performance was self-evaluated by a questionnaire. Speech in noise and sound localization was compared with young normal hearing adults. Although considering themselves as hearing normal, 17 subjects had at least a mild hearing loss. There was a significant negative correlation between hearing loss and dementia screening score. Speech perception in noise decreased significantly with age. There were significant negative correlations between speech perception in noise and DemTect score for both spatial configurations. Mean SRTs obtained in the co-located noise condition with amplitude-modulated noise were on average 3.1 dB better than with continuous noise. This gap-listening effect was severely diminished compared to a younger normal hearing subject group. In continuous noise, spatial separation of speech and noise led to better SRTs compared to the co-located masker condition. SRTs in MSNF deteriorated in modulated noise compared to continuous noise by 2.6 dB. Highest impact of age was found for speech perception scores using noise stimuli with temporal modulation in binaural test conditions. Mean localization error was in the range of young adults. Mean amount of front/back confusions was 11.5% higher than for young adults. Speech perception tests in the presence of temporally modulated noise can serve as a screening method for early detection of hearing disorders in older adults. This allows for early prescription of hearing aids. (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)

The relationship between the neurobiology of speech and music has been investigated for more than a century. There remains no widespread agreement regarding how (or to what extent) music perception utilizes the neural circuitry that is engaged in speech processing, particularly at the cortical level. Prominent models such as Patel’s Shared Syntactic Integration Resource Hypothesis (SSIRH) and Koelsch’s neurocognitive model of music perception suggest a high degree of overlap, particularly in the frontal lobe, but also perhaps more distinct (...) representations in the temporal lobe with hemispheric asymmetries. The present meta-analysis study used activation likelihood estimate analyses to identify the brain regions consistently activated for music as compared to speech across the functional neuroimaging (fMRI and PET) literature. Eighty music and 91 speech neuroimaging studies of healthy adult control subjects were analyzed. Peak activations reported in the music and speech studies were divided into four paradigm categories: passive listening, discrimination tasks, error/anomaly detection tasks and memory-related tasks. We then compared activation likelihood estimates within each category for music versus speech, and each music condition with passive listening. We found that listening to music and to speech preferentially activate distinct temporo-parietal bilateral cortical networks. We also found music and speech to have shared resources in the left pars opercularis but speech-specific resources in the left pars triangularis. The extent to which music recruited speech-activated frontal resources was modulated by task. While there are certainly limitations to meta-analysis techniques particularly regarding sensitivity, this work suggests that the extent of shared resources between speech and music may be task-dependent and highlights the need to consider how task effects may be affecting conclusions regarding the neurobiology of speech and music. (shrink)

In this study, multidimensional feature extraction is performed on the U-language recordings of the test takers, and these features are evaluated separately, with five categories of features: pronunciation, fluency, vocabulary, grammar, and semantics. A deep neural network model is constructed to model the feature values to obtain the final score. Based on the previous research, this study uses a deep neural network training model instead of linear regression to improve the correlation between model score and expert score. The method of (...) using word frequency for semantic scoring is replaced by the LDA topic model for semantic analysis, which eliminates the need for experts to manually label keywords before scoring and truly automates the critique. Also, this paper introduces text cleaning after speech recognition and deep learning-based speech noise reduction technology in the scoring model, which improves the accuracy of speech recognition and the overall accuracy of the scoring model. Also, innovative applications and improvements are made to key technologies, and the latest technical solutions are integrated and improved. A new open oral grading model is proposed and implemented, and innovations are made in the method of speech feature extraction to improve the dimensionality of open oral grading. (shrink)