Nabil N. Bitar
Dept. of Elect., Comput. and Systems Eng., Boston Univ., 44 Cummington St., Boston, MA 02215
As part of a feature-based speech recognition system, a broad classifier that automatically labels the speech signal in terms of one of the six categories silence, stop, fricative, vowel, semivowel, and nasal was developed. The classifier is based on the detection of acoustic correlates for the linguistic features consonantal, continuant, nonsyllabic, and sonorant. Acoustic properties for the features are detected on the basis of events such as minima, maxima, or changes from a low to high value (or vice versa) in some defined signal parameters (e.g., low-frequency energy). These landmarks may point up particular instants in time, or they may define regions within the waveform. Fuzzy logic is used to represent the fact that features are manifest in the signal with varying degrees of strength. Preliminary results with the TIMIT database are promising and our analysis shows that phenomena such as coarticulation and phonetic lenition are better handled if all of the acoustic properties are extracted in parallel so that a decision about a speech category is not made until all information is available. [This research was supported by NSF Research Grant No. BNS-8920470.]