Departmental Papers (ESE)


In this work, a feature-based system for the automatic classification of stop consonants, in speaker independent continuous speech, is reported. The system uses a new auditory-based speech processing front-end that is based on the biologically rooted property of average localized synchrony detection (ALSD). It incorporates new algorithms for the extraction and manipulation of the acoustic-phonetic features that proved, statistically, to be rich in their information content. The experiments are performed on stop consonants extracted from the TIMIT database with additive white Gaussian noise at various signal-to-noise ratios. The obtained classification accuracy compares favorably with previous work. The results also showed a consistent improvement of 3% in the place detection over the Generalized Synchrony Detector (GSD) system under identical circumstances on clean and noisy speech. This illustrates the superior ability of the ALSD to suppress the spurious peaks and produce a consistent and robust formant (peak) representation.

Document Type

Conference Paper

Date of this Version

May 2001


Copyright 2001 IEEE. Reprinted from Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing 2001 (ICASSP 2001) Volume 1, pages 81-84.

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features, stop consonants, speaker independent speech, speech recognition, auditory-based speech processing, acoutic-phonetic feature, average localized synchrony, ALSD



Date Posted: 21 November 2004

This document has been peer reviewed.