An event-based acoustic-phonetic approach for speech segmentation and E-set recognition

In this paper, we discuss an event-based recognition system (EBS) which is based on phonetic feature theory and acoustic phonetics. First, acoustic events related to the manner phonetic features are extracted from the speech signal. Second, based on the manner acoustic events, information related to the place phonetic features and voicing are extracted. Most recently, we focused on place and voicing information needed to distinguish among the stop consonants /t,d,p,b/. Using the E-set utterances from the TI46 database, EBS achieved 75.7% overall word accuracy. Further, the knowledge-based acoustic parameters (APs) optimized within the EBS framework were compared to the mel-frequency cepstral coefficients in an HMM-based recognition system. The results on the E-set task showed that the APs achieve a higher recognition accuracy.