Fuzzy string kernel representations in speech processing

Auteurs-es

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

Mots-clés :

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

Résumé

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.

Fichiers supplémentaires

Publié-e

2003-09-01

Comment citer

1.
Kirchner R. Fuzzy string kernel representations in speech processing. Canadian Acoustics [Internet]. 1 sept. 2003 [cité 13 mai 2026];31(3):38-9. Disponible à: https://jcaa.caa-aca.ca/index.php/jcaa/article/view/1539

Numéro

Vol. 31 No. 3 (2003)

Rubrique

Actes du congrès de la Semaine canadienne d'acoustique

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