NDLI: Robust classification of stop consonants using auditory-based speech processing

Content Provider	IEEE Xplore Digital Library
Author	Ali, A.M.A. Van der Spiegel, J. Mueller, P.
Copyright Year	2001
Description	Author affiliation: Texas Instrum. Inc., Warren, NJ, USA (Ali, A.M.A.)
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.
Sponsorship	IEEE Signal Process. Soc
Starting Page	81
Ending Page	84
File Size	431600
Page Count	4
File Format	PDF
ISBN	0780370414
ISSN	15206149
DOI	10.1109/ICASSP.2001.940772
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-05-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Speech processing Acoustic signal detection Acoustic noise Automatic speech recognition Filter bank Frequency synchronization Data mining Detectors Speech recognition
Content Type	Text
Resource Type	Article

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