Abstract

A probabilistic and statistical framework is presented for automatic speech recognition based on a phonetic feature representation of speech sounds. In this acoustic-phonetic approach, the speech recognition problem is hypothesized as a maximization of the joint posterior probability of a set of phonetic features and the corresponding acoustic landmarks. Binary classifiers of the manner phonetic features- syllabic, sonorant and continuant- are applied for the probabilistic detec-tion of speech landmarks. The landmarks include stop bursts, vowel onsets, syllabic peaks, syllabic dips, fricative onsets and offsets, and sonorant consonant onsets and offsets. The classifiers use automatically extracted knowledge based acoustic param-eters (APs) that are acoustic correlates of those phonetic features. For isolated word recognition with known and limited vocabulary, the landmark sequences are con-strained using a manner class pronunciation graph. Probabilistic decisions on place and voicing phonetic features are then made using a separate set of APs extracted using the landmarks. The framework exploits two properties of the knowledge-based acoustic cues