Spoken Language Processing by Xuedong Huang

By Xuedong Huang

Speech processing addresses quite a few medical and technological parts. It contains speech research and variable expense coding, to be able to shop or transmit speech. It additionally covers speech synthesis, particularly from textual content, speech attractiveness, together with speaker and language identity, and spoken language understanding.
This ebook covers the subsequent issues: the way to become aware of speech construction and notion platforms, tips on how to synthesize and comprehend speech utilizing cutting-edge tools in sign processing, development popularity, stochastic modelling computational linguistics and human issue studies.Content:
Chapter 1 Speech research (pages 1–53): Christophe D'Alessandro
Chapter 2 ideas of Speech Coding (pages 55–98): Gang Feng and Laurent Girin
Chapter three Speech Synthesis (pages 99–167): Olivier Boeuffard and Christophe D'Alessandro
Chapter four Facial Animation for visible Speech (pages 169–187): Thierry Guiard?Marigny
Chapter five Computational Auditory Scene research (pages 189–211): Alain De Cheveigne
Chapter 6 ideas of Speech reputation (pages 213–238): Renato De Mori and Brigitte Bigi
Chapter 7 Speech acceptance platforms (pages 239–278): Jean?Luc Gauvain and Lori Lamel
Chapter eight Language id (pages 279–320): Martine Adda?Decker
Chapter nine computerized Speaker acceptance (pages 321–354): Frederic Bimbot
Chapter 10 strong acceptance tools (pages 355–375): Jean?Paul Haton
Chapter eleven Multimodal Speech: or 3 Senses are larger than One (pages 377–415): Jean?Luc Schwartz, Pierre Escudier and Pascal Teissier
Chapter 12 Speech and Human?Computer verbal exchange (pages 417–454): Wolfgang Minker and Francoise Neel
Chapter thirteen Voice companies within the Telecom zone (pages 455–466): Laurent Courtois, Patrick Brisard and Christian Gagnoulet

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Extra info for Spoken Language Processing

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It is based on the sourcefilter model. The speech signal is written as an excitation e(t) passed through a filter with an impulse response h(t, τ), which evolves over time. 104] It must be noted that the number L(t) of sinusoidal segments in the excitation varies with time, as well as amplitudes al and frequencies ωl. The initial phases φl depend on the starting time of sinusoid tl. 105]. The number L(t) of tracks varies with time: each track is active during a given lapse of time and this has to be determined by a tracking algorithm.

In the Burg method, the calculation of the reflection coefficients is based on the minimization (in the least squares sense) of the sum of the forward and backward errors. 66] Speech Analysis 31 These coefficients no longer correspond to the autocorrelation method, but they possess good stability properties, as it can be shown that −1 ≤ kn ≤ 1. Adaptive versions of the Burg algorithm also exist [MAK 75, MAK 81]. 4. Models of the excitation In addition to the filter part of the linear prediction model, the source part has to be estimated.

However, none of these methods have so far reached a level of expertise comparable to that of the spectrogram, which, despite its lower accuracy in certain situations, remains more usual and more conventional, from a practical point of view. 2. Wavelets The wavelet transform is a “time-scale” method [COM 89, MEY 90]. 94] 42 Spoken Language Processing These are methods with a constant relative resolution in the frequency domain, (ν/νc= constant, where ν is the bandwidth of the filters and νc their central frequency), similarly to the acoustic analysis using a third of one-third octave band filters.

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