Speech production knowledge in automatic speech recognition.
Although much is known about how speech is produced, and research into speech production has resulted in measured articulatory data, feature systems of different kinds, and numerous models, speech production knowledge is almost totally ignored in current mainstream approaches to automatic speech recognition. Representations of speech production allow simple explanations for many phenomena observed in speech which cannot be easily analyzed from either acoustic signal or phonetic transcription alone. In this article, a survey of a growing body of work in which such representations are used to improve automatic speech recognition is provided.
King
Simon
S
Centre for Speech Technology Research, University of Edinburgh, 2 Buccleuch Place, Edinburgh EH8 9LW, United Kingdom. Simon.King@ed.ac.uk
Frankel
Joe
J
Livescu
Karen
K
McDermott
Erik
E
Richmond
Korin
K
Wester
Mirjam
M
D001499:Bayes Theorem; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D016571:Neural Networks, Computer; D010699:Phonation; D010700:Phonetics; D012660:Semantics; D013018:Sound Spectrography; D013061:Speech Acoustics; D013062:Speech Articulation Tests; D013068:Speech Production Measurement; D049250:Speech Recognition Software