Knowledge-based parameters for HMM speech recognition

This paper presents acoustic parameters (APs) that were motivated by phonetic feature theory and employed as a signal representation of speech in a hidden Markov model (HMM) recognition framework. Presently, the phonetic features considered are the manner features: sonorant, syllabic, nonsyllabic, noncontinuant and fricated. The objective of the parameters is to directly target the linguistic information in the signal and to reduce the speaker-dependent information that may yield large speech variability. To achieve these goals, the APs were defined in a relational manner across time or frequency. For evaluation, broad-class recognition experiments were conducted comparing the APs to cepstral-based parameters. The results of the experiments indicate that the APs are able to capture the phonetically relevant information in the speech signal and that, in comparison to the cepstral-based parameters, they are more able to reduce the interspeaker variability