title: Speech production knowledge in automatic speech recognition.
abstract: 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.
authors.affiliation: Centre for Speech Technology Research, University of Edinburgh, 2 Buccleuch Place, Edinburgh EH8 9LW, United Kingdom. Simon.King@ed.ac.uk
authors.forename: Simon
authors.initials: S
authors.lastname: King
authors.forename: Joe
authors.initials: J
authors.lastname: Frankel
authors.forename: Karen
authors.initials: K
authors.lastname: Livescu
authors.forename: Erik
authors.initials: E
authors.lastname: McDermott
authors.forename: Korin
authors.initials: K
authors.lastname: Richmond
authors.forename: Mirjam
authors.initials: M
authors.lastname: Wester
mesh_terms: 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