Acoustic-phonetic features for the automatic classification of stop consonants

In this paper, the acoustic-phonetic characteristics of the American Englis h stop consonants are investigated. Features studied in the literature are evaluated for their information content and new features are proposed. A st atistically guided, knowledge-based, acoustic-phonetic system for the autom atic classification of stops, in speaker independent continuous speech, is proposed. The system uses a new auditory-based front-end processing and inc orporates new algorithms for the extraction and manipulation of the acousti c-phonetic features that proved to be rich in their information content. Re cognition experiments are performed using hard decision algorithms on stops extracted from the TIMIT database continuous speech of 60 speakers (not us ed in the design process) from seven different dialects of American English . An accuracy of 96% is obtained for voicing detection, 90% for place of ar ticulation detection and 86% for the overall classification of stops.