Subphonetic modeling with Markov states-Senone

There will never be sufficient training data to model all the various acoustic-phonetic phenomena. How to capture important clues and estimate those needed parameters reliably is one of the central issues in speech recognition. Successful examples include subword models, fenones and many other smoothing techniques. In comparison with subword models, subphonetic modeling may provide a finer level of details. The authors propose to model subphonetic events with Markov states and treat the state in phonetic hidden Markov models as the basic subphonetic unit-senone. Senones generalize fenones in several ways. A word model is a concatenation of senones and senones can be shared across different word models. Senone models not only allow parameter sharing, but also enable pronunciation optimization. The authors report preliminary senone modeling results, which have significantly reduced the word error rate for speaker-independent continuous speech recognition