|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
EXPLOITING VARIABLE WIDTH FEATURES IN LARGE VOCABULARY SPEECH RECOGNITION
M. Jones and P.C. Woodland
The use of variable-width features (prosodics, broad structural information etc.) in large vocabulary speech recognition systems is discussed. Although the value of this sort of information has been recognised in the past, previous approaches have not been widely used in speech systems because either they have not been robust enough for realistic, large vocabulary tasks or they have been limited to certain recogniser architectures. A framework for the use of variable-width features is presented which employs the N-Best algorithm with the features being applied in a post-processing phase. The framework is flexible and widely applicable, giving greater scope for exploitation of the features than previous approaches. Large vocabulary speech recognition experiments using TIMIT show that the application of variable-width features has potential benefits.
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2005 Cambridge University Engineering Dept
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