Departmental Papers (ESE)


In this paper, the acoustic–phonetic characteristics of American English stop consonants are investigated. Features studied in the literature are evaluated for their information content and new features are proposed. A statistically guided, knowledge-based, acoustic–phonetic system for the automatic classification of stops, in speaker independent continuous speech, is proposed. The system uses a new auditory-based front-end processing and incorporates new algorithms for the extraction and manipulation of the acoustic–phonetic features that proved to be rich in their information content. Recognition experiments are performed using hard decision algorithms on stops extracted from the TIMIT database continuous speech of 60 speakers (not used in the design process) from seven different dialects of American English. An accuracy of 96% is obtained for voicing detection, 90% for place articulation detection and 86% for the overall classification of stops.

Document Type

Journal Article

Date of this Version



Copyright 2001 IEEE. Reprinted from IEEE Transactions on Speech and Audio Processing, Volume 9, Issue 8, November 2001, pages 833-841.
Publisher URL:

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 By choosing to view this document, you agree to all provisions of the copyright laws protecting it.


Acoustic–phonetic, feature extraction, phoneme recognition, speech recognition, stop consonants



Date Posted: 09 November 2004

This document has been peer reviewed.