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Acoustical and Environmental Robustness in Automatic Speech Recognition (Paperback, Softcover reprint of the original 1st ed. 1993)
Loot Price: R4,324
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Acoustical and Environmental Robustness in Automatic Speech Recognition (Paperback, Softcover reprint of the original 1st ed. 1993)
Series: The Springer International Series in Engineering and Computer Science, 201
Expected to ship within 10 - 15 working days
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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.
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