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The motivation of this study is the poor performance of speech recognizers on the stop consonants. To overcome this weakness, word initial and word final stop consonants are modeled at a subphonemic (microsegmental) level. Each stop consonant is segmented into a few relatively stationary microsegments: silence, voice bar, burst, and aspiration. Microsegments of certain phonemically different stops are trained together due to their similar spectral properties. Microsegmental models of burst and aspiration are conditioned on the adjacent vowel category: front versus nonfront vowels. The resulting context-dependent microsegmental hidden Markov models (HMMs) for six stops possess the desired properties for a compromise between modeling accuracy and modeling robustness. They allow the recognizer to focus discrimination onto those regions of a stop that serve to distinguish it from other stops. Use of these models in recognition experiments for word lists consisting of CVC words reduces the error rate by 35% compared with the result obtained by using one HMM for each stop phoneme.
Deng et al. (Fri,) studied this question.