SIGNIFICANCE OF INVARIANT ACOUSTIC CUES IN A PROBABILISTIC FRAMEWORK FOR LANDMARK-BASED SPEECH RECOGNITION
Amit Juneja, Carol Espy-Wilson · 2004
A probabilistic framework for landmark-based speech recognition that utilizes the sufficiency and context invariance properties of acoustic cues for phonetic features is presented. Binary classifiers of the manner phonetic features sonorant, continuant and syllabic operate on each frame of speech, each using a small number of relevant and sufficient acoustic parameters to generate probabilistic landmark sequences. The relative nature of the parameters developed for the extraction of acoustic cues for manner phonetic features makes them of the manner of neighboring speech frames. This invariance of manner acoustic cues makes the use of only those three classifiers along with the speech/silence classifier complete irrespective of the manner context. The obtained landmarks are then used to extract relevant acoustic cues to make probabilistic binary decisions for the place and voicing phonetic features. Similar to the invariance property of the manner acoustic cues, the acoustic cues for place phonetic features extracted using manner landmarks are invariant of the place of neighboring sounds. Pronunciation models based on phonetic features are used to constrain the landmark sequences and to narrow the classification of place and voicing. Preliminary results have been obtained for manner recognition and the corresponding landmarks. Using classifiers trained from the phonetically rich TIMIT database, 80.2% accuracy was obtained for broad class recognition of the isolated digits in the TIDIGITS database which compares well with the accuracies of 74.8% and 81.0% obtained by a hidden Markov model (HMM) based system using mel-frequency cepstral coefficients (MFCCs) and knowledge-based parameters, respectively.