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ACOUSTIC MODELS OF CV SYLLABLES FOR SPEECH RECOGNITION

Auditory identification of phonemes depends on the target phoneme and on the signal surrounding it. Two sources of information have been proposed to explain this fact: a) coarticulatory information in adjacent segments; b) the phonetic identity of the segments. It is not clear yet if the target and adjacent segments are processed jointly, or if they are processed independently and the corresponding information is later combined. Speech recognition systems based on context-dependent phonetic models opt for the later alternative, and the information is combined assuming that the target and adjacent segments are independent. We propose models of interaction that take into account both the coarticulatory information and the phonetic identity of the segments, processed either independently or jointly. The acoustic characterization of the segments was based on the coding of the spectral energy. The models were first tested in fricative-vowel CV syllables, showing that the best automatic recognition rates were achieved when both sources of information were included and the C and V segments were processed jointly. Later, this model was tested with a database of continuous speech and compared with the performance of a context-dependent HMM-based system: 25% and 17% reduction in error rate was achieved for clean speech and for speech in noise, respectively.