Knowledge-based approaches in acoustic-phonetic decoding of speech

The level of acoustic-phonetic decoding (i.e. the transformation of the acoustic continuum of speech into a description under the form of discrete, linguistic units) represents an important step and a major bottleneck in the overall process of automatic speech recognition.

[1]  Carlo Scagliola.  Continuous speech recognition without segmentation: Two ways of using diphones as basic speech units , 1983, Speech Commun..

[2]  R. Bakis.  Continuous speech recognition via centisecond acoustic states , 1976 .

[3]  Stephen E. Levinson,et al.  Continuously variable duration hidden Markov models for speech analysis , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[4]  Renato De Mori,et al.  A parser for segmenting continuous speech into pseudo-syllabic nuclei , 1980, ICASSP.

[5]  Jean-Paul Haton.  Fundamentals in computer understanding: speech and vision , 1987 .

[6]  M. Eskenazi,et al.  Un systeme expert pour la lecture de sonagrammes , 1983, Speech Commun..

[7]  J. Simon.  Spoken Language Generation and Understanding , 1980 .

[8]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[9]  J. Damestoy,et al.  A frame language for the control of phonetic decoding in continuous speech recognition , 1985, ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[10]  Renato De Mori,et al.  Integration of acoustic, phonetic, prosodic and lexical knowledge in an expert system for speech understanding , 1984, ICASSP.

[11]  N. Carbonell,et al.  Interaction between stochastic modeling and knowledge-based techniques in acoustic-phonetic decoding of speech , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[12]  R. Pieraccini,et al.  Definition and evaluation of phonetic units for speech recognition by hidden Markov models , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[13]  Jean-Paul Haton,et al.  Automatic Speech Analysis and Recognition , 1982 .

[14]  Renato De Mori.  Extraction of acoustic cues using a grammar of frames , 1983, Speech Commun..

[15]  G. Mercier,et al.  The KEAL Speech Understanding System , 1980 .

[16]  Riichiro Mizoguchi,et al.  A continuous speech recognition system based on knowledge engineering techniques , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[17]  L. R. Rabiner,et al.  An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition , 1983, The Bell System Technical Journal.

[18]  Dennis H. Klatt,et al.  Review of the ARPA speech understanding project , 1990 .

[19]  Günther Ruske.  Automatic recognition of syllabic speech segments using spectral and temporal features , 1982, ICASSP.

[20]  F. Jelinek,et al.  Continuous speech recognition by statistical methods , 1976, Proceedings of the IEEE.

[21]  T. G. V. Keller.  An On‐Line Recognition System for Spoken Digits , 1971 .

[22]  Noëlle Carbonell,et al.  Aphodex, an acoustic-phonetic Decoding Expert System , 1987, Int. J. Pattern Recognit. Artif. Intell..

[23]  Ronald A. Cole,et al.  Experiments on spectrogram reading , 1979, ICASSP.

[24]  Henry Gilbert Goldberg,et al.  Segmentation and labeling of speech: a comparative performance evaluation. , 1976 .

[25]  Wayne A. Lea,et al.  Trends in Speech Recognition , 1980 .

[26]  Phil D. Green,et al.  A speech recognition strategy based on making acoustic evidence and phonetic knowledge explicit , 1987, ECST.

[27]  Guy Perennou,et al.  Structuration des informations acoustiques dans le projet A.R.I.A.L , 1983, Speech Commun..