Fuzzy string kernel representations in speech processing

Authors

  • Robert Kirchner Linguistics Dept., University of Alberta, Edmonton, Alta. T6G2E1, Canada

Keywords:

Fuzzy sets, Learning systems, Neural networks, Pattern recognition, Vector quantization, Speech signal, Support vector machines

Abstract

Many widely used approaches to pattern recognition/machine learning, including neural nets, k-nearest-neighbours classifiers, and support vector machines, have hitherto made little headway in speech processing, largely due to their inability to represent and compute over the variable-length sequential data of speech signals. A new technique, the string (subsequence) kernel, first applied in bioinformatics [1] and text classification [2], and extended to speech recognition by Goddard et al. [3], maps a variable-length input signal to a fixed-length feature array, by taking the inner product of n-gram subsequences. Similarity of signals can then be evaluated, by any of the above approaches, in the kernel space. In this presentation, two variations on Goddard's approach are considered and evaluated: a string kernel using fuzzy rather than absolute k-means clustering; and a kernel in which the feature counts are preserved as waveforms rather than scalars, to address the reverse mapping problem.

Additional Files

Published

2003-09-01

How to Cite

1.
Kirchner R. Fuzzy string kernel representations in speech processing. Canadian Acoustics [Internet]. 2003 Sep. 1 [cited 2026 May 6];31(3):38-9. Available from: https://jcaa.caa-aca.ca/index.php/jcaa/article/view/1539

Issue

Vol. 31 No. 3 (2003)

Section

Proceedings of the Acoustics Week in Canada

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