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Acoustical And Environmental Robustness In Automatic Speech Recognition (Cód: 6109046)

Alejandro Acero; A. Acero


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Acoustical And Environmental Robustness In Automatic Speech Recognition



The need for automatic speech recognition systems to be robust with respect to changes in their acoustical environment has become more widely appreciated in recent years, as more systems are finding their way into
practical applications. Although the issue of environmental robustness has received only a small fraction of the attention devoted to speaker independence, even speech recognition systems that are designed to be speaker
independent frequently perform very poorly when they are tested using a different type of microphone or acoustical environment from the one with which they were trained. There are several different ways of building
acoustical robustness into speech recognition systems. Acoustical and Environmental Robustness in Automatic Speech Recognition employs the approach of transforming
speech recorded from a single microphone in the application environment so that it more closely matches the important acoustical characteristics of the speech that was used to train the recognition system. The book builds
on the older techniques of spectral subtraction and spectral normalization, which were originally developed to enhance the quality of degraded speech for human listeners. Spectral subtraction and spectral normalization were
designed to ameliorate the effects of two complementary types of environmental degradation: additive noise and unknown linear filtering. The most important contribution in this book is the development of a family of
algorithms that jointly compensate for the effects of these two types of degradation. This unified approach to signal normalization provides significantly better recognition accuracy than the independent compensation
strategies developed in prior research. The algorithms described in this monograph, such as codeword-dependent cepstral normalization (CDCN) and blind signal-to-noise-ratio cepstral normalization (BSDCN), have
been shown to provide major improvements in recognition accuracy for speech systems in offices using desktop microphones, in automobiles, and over telephone lines. Although originally developed for speech recognition
systems using discrete hidden Markow models, these algorithms are effective when applied to systems that use semi-continuous hidden Markow models as well. Real-time implementations have been developed for the compensation
algorithms using workstations with onboard digital signal processors. Acoustical and Environmental Robustness in Automatic Speech Recognition provides a comprehensive
review and comparison of the major single-channel compensation strategies currently in the literature. It develops a unified cepstral respresentation that facilitates joint compensation for the effects of noise, filtering
and frequency warping. Finally, it describes and explains the compensation algorithms that have been developed to compensate for these types of environmental degradation, and it provides the details needed to implement the
algorithms. As such, the book serves as an excellent reference and may be used as the text for an advanced course on the subject.


Produto sob encomenda Sim
Cód. Barras 9780792392842
Altura 23.40 cm
I.S.B.N. 9780792392842
Profundidade 1.27 cm
Referência 9780792392842
Ano da edição 1992
Idioma Inglês
Número de Páginas 212
Peso 0.45 Kg
Largura 15.60 cm
AutorAlejandro Acero; A. Acero