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Connectionist Speech Recognition - A Hybrid Approach (Hardcover, 1994 ed.)
Loot Price: R6,329
Discovery Miles 63 290
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Connectionist Speech Recognition - A Hybrid Approach (Hardcover, 1994 ed.)
Series: The Springer International Series in Engineering and Computer Science, 247
Expected to ship within 12 - 17 working days
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Total price: R6,349
Discovery Miles: 63 490
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Connectionist Speech Recognition: A Hybrid Approach describes the
theory and implementation of a method to incorporate neural network
approaches into state of the art continuous speech recognition
systems based on hidden Markov models (HMMs) to improve their
performance. In this framework, neural networks (and in particular,
multilayer perceptrons or MLPs) have been restricted to
well-defined subtasks of the whole system, i.e. HMM emission
probability estimation and feature extraction. The book describes a
successful five-year international collaboration between the
authors. The lessons learned form a case study that demonstrates
how hybrid systems can be developed to combine neural networks with
more traditional statistical approaches. The book illustrates both
the advantages and limitations of neural networks in the framework
of a statistical systems. Using standard databases and comparison
with some conventional approaches, it is shown that MLP probability
estimation can improve recognition performance. Other approaches
are discussed, though there is no such unequivocal experimental
result for these methods. Connectionist Speech Recognition is of
use to anyone intending to use neural networks for speech
recognition or within the framework provided by an existing
successful statistical approach. This includes research and
development groups working in the field of speech recognition, both
with standard and neural network approaches, as well as other
pattern recognition and/or neural network researchers. The book is
also suitable as a text for advanced courses on neural networks or
speech processing.
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