Robust Classification of Stop Consonants Using Auditory-Based Speech Processing
speaker independent speech
auditory-based speech processing
average localized synchrony
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.
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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. Publisher URL: http://ieeexplore.ieee.org/xpl/tocresult.jsp?isNumber=20365&page=1 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to email@example.com. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.