Get Automatic Speech and Speaker Recognition: Advanced Topics PDF

By L. R. Rabiner, B.-H. Juang, C.-H. Lee (auth.), Chin-Hui Lee, Frank K. Soong, Kuldip K. Paliwal (eds.)

ISBN-10: 1461285909

ISBN-13: 9781461285908

ISBN-10: 1461313678

ISBN-13: 9781461313670

Research within the box of automated speech and speaker attractiveness has made a few major advances within the final 20 years, inspired via advances in sign processing, algorithms, architectures, and undefined. those advances contain: the adoption of a statistical trend acceptance paradigm; using the hidden Markov modeling framework to symbolize either the spectral and the temporal adaptations within the speech sign; using a wide set of speech utterance examples from a wide inhabitants of audio system to coach the hidden Markov versions of a few primary speech devices; the association of speech and language wisdom resources right into a structural finite kingdom community; and using dynamic, programming dependent heuristic seek the right way to locate the simplest observe series within the lexical community reminiscent of the spoken utterance.
Automatic Speech and Speaker popularity: complex Topics teams jointly in one quantity a few vital subject matters on speech and speaker acceptance, themes that are of primary significance, yet no longer but lined intimately in current textbooks. even though no particular partition is given, the booklet is split into 5 elements: Chapters 1-2 are dedicated to know-how overviews; Chapters 3-12 speak about acoustic modeling of primary speech devices and lexical modeling of phrases and pronunciations; Chapters 13-15 deal with the problems with regards to flexibility and robustness; bankruptcy 16-18 hindrance the theoretical and sensible problems with seek; Chapters 19-20 provide examples of set of rules and implementational elements for reputation process awareness.
Audience: A reference booklet for speech researchers and graduate scholars attracted to pursuing strength learn at the subject. can also be used as a textual content for complicated classes at the subject.

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Picheny and L. R. Bahl, "The Metamorphic Algorithm: A Speaker Mapping Approach to Data Augmentation," IEEE Trans. , Vol. 2, pp. 413-420, 1994. [11] A. Biem, S. -H. Juang, "Discriminative Feature Extraction for Speech Recognition," Proc. IEEE NN-SP Workshop, 1993. [12] H. Bourlard and C. J. Wellekens, "Links between Markov Models and Multi-Layer Perceptron," IEEE Trans. Pattern Analysis, Machine Intelligence, Vol. 12, pp. 1167-1178,1992. [13] H. Bourlard and N. Morgan, Connectionist Speech Recognition - A Hybrid Approach, Kluwer Academic Publishers, 1994.

The measure of recognizer performance is the word error rate (in percent) for a given vocabulary, task perplexity, and syntax (grammar). 1%). 0% for the SI mode. Considering the confusability among spoken letters, these results are actually quite impressive for telephone bandwidth speech. 3% (SD) with a limit amount of training data. 7% (SI) is achieved for a vocabulary of 1218 town names using vocabulary-independent training. 2% (SI). 1%. For fluent speech recognition, results are based on DARPA funded research on three tasks; namely a ships database task (Naval Resource Management), an airline travel task (ATIS), and speech read from the Wall Street Journal.

A spectral envelope reconstructed from a truncated set of cepstral coefficients is much smoother than one reconstructed from LPC coefficients, and therefore provides a stabler representation from one repetition to another of a particular speaker's utterances. For the regression coefficients, typically, the first- and second-order coefficients, that is, derivatives of the time functions of cepstral coefficients are extracted at every frame period to represent spectral dynamics. These are respectively called the delta- and delta-delta-cepstral coefficients.

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Automatic Speech and Speaker Recognition: Advanced Topics by L. R. Rabiner, B.-H. Juang, C.-H. Lee (auth.), Chin-Hui Lee, Frank K. Soong, Kuldip K. Paliwal (eds.)

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