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Robust Classification of Stop Consonants Using Auditory-Based Speech Processing
Departmental Papers (ESE)
  • Ahmed M Abdelatty Ali, University of Pennsylvania
  • Jan Van der Spiegel, University of Pennsylvania
  • Paul Mueller, Corticon, Inc.
Abstract
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
5-7-2001
Comments
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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Keywords
  • features,
  • stop consonants,
  • speaker independent speech,
  • speech recognition,
  • auditory-based speech processing,
  • acoutic-phonetic feature,
  • average localized synchrony,
  • ALSD
Citation Information
Ahmed M Abdelatty Ali, Jan Van der Spiegel and Paul Mueller. "Robust Classification of Stop Consonants Using Auditory-Based Speech Processing" (2001)
Available at: http://works.bepress.com/jan_vanderspiegel/5/