Locus equation and hidden parameters of speech
Behavioral and Brain Sciences 21 (2):263-264 (1998)
| Abstract | Locus equations contain an economical set of hidden (i.e., not directly observable in the data) parameters of speech that provide an elegant way of characterizing the ubiquitous context-dependent behaviors exhibited in speech acoustics. These hidden parameters can be effectively exploited to constrain the huge set of context-dependent speech model parameters currently in use in modern, mainstream speech recognition technology. | |||||||||
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John J. Ohala (1998). Content First, Frame Later. Behavioral and Brain Sciences 21 (4):525-526.
James R. Sawusch (1998). Acoustic Correlates and Perceptual Cues in Speech. Behavioral and Brain Sciences 21 (2):283-284.
Sheila E. Blumstein (1998). The Mapping From Acoustic Structure to the Phonetic Categories of Speech: The Invariance Problem. Behavioral and Brain Sciences 21 (2):260-260.
Jagmeet S. Kanwal (1998). Charting Speech with Bats Without Requiring Maps. Behavioral and Brain Sciences 21 (2):272-273.
Archana Balyan, S. S. Agrawal & Amita Dev (2012). Automatic Phonetic Segmentation of Hindi Speech Using Hidden Markov Model. AI and Society 27 (4):543-549.
René Carré (1998). Linear Correlates in the Speech Signal: Consequences of the Specific Use of an Acoustic Tube? Behavioral and Brain Sciences 21 (2):261-262.
Richard E. Pastore & Edward J. Crawley (1998). Locus Equation: Assumption and Dependencies. Behavioral and Brain Sciences 21 (2):278-279.
Frank H. Guenther (1998). An Account of the Locus Equation Phenomenon Based on Speech Movement Planning. Behavioral and Brain Sciences 21 (2):268-269.
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