Automatic recognition of syllable-final nasals preceded by /epsilon/.

In this paper, it is shown how an automatic recognition algorithm, based on hidden Markov models (HMM), can benefit by properly utilizing findings from perceptual experiments on nasals. Perceptual studies on nasal consonants have shown that both nasal murmurs and formant transitions are important in the identification of place of articulation. Thus both acoustic segments bordering the nasal release were incorporated into this HMM-based system. A 7% improvement in alveolar recognition was obtained by explicitly modeling the vowel-nasal transition segments. Further overall improvement (6%) was realized by making the HMM recognizer "focus" more on the vowel-nasal transition segments bordering the nasal release, and less on the nasal murmur and vowel portion of the /epsilon m/ and /epsilon n/ syllables. An overall average [m]-[n] recognition of 95% was obtained when testing this technique on 60 speakers outside the training set.