Title: Acoustic-phonetic speech parameters for speaker-independent speech recognition
Abstract: Coping with inter-speaker variability (i.e., differences in the vocal tract characteristics of speakers) is still a major challenge for Automatic Speech Recognizers. In this paper, we discuss a method that compensates for differences in speaker characteristics. In particular, we demonstrate that when continuous density hidden Markov model based system is used as the back-end, a Knowledge-Based Front End (KBFE) can outperform the traditional Mel-Frequency Cepstral Coefficients (MFCCs), particularly when there is a mismatch in the gender and ages of the subjects used to train and test the recognizer. This work was supported by NSF grant # SBR-9729688 and NIH grant # IK02DCOOI49.
Publication Year: 2002
Publication Date: 2002-05-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 21
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