Modeling contextual variations of phones is widely accepted as an important aspect of a continuous speech recognition system, and much research has been devoted to finding robust models of context for HMhiI systems. In particular, decision tree clustering has been used to tie output distributions across pre-defined states, and successive state splitting (SSS) has been used to define parsimonious HMM topologies. In this paper, we describe a new HMM design algorithm, called maximum likelihood successive state splitting (ML-SSS), that combines advantages of both these approaches. Specifically, an HMM topology is designed using a greedy search for the best temporal and contextual splits using a coiistrained EM algorithm. In Japanese phone recognition experiments, ML-SSS shows recognition performance gains and training cost reduction over SSS under several training conditions. 0-7803-3192-3/96 $5.0001996 DeEE
This paper proposes a novel method of incorporating pitch information into an HMM speech recognition system by exploiting the correlation between pitch and spectral parameters, e.g. cepstrum. Pitch patterns are not used explicitly; instead, spectral parameters are normalized framewise according to the pitch value. Evidence is given to show that the use of pitch information consistently improves the recognition performance. Experiments with 24 phoneme labels showed that the phoneme error rate for fast continuous speech could be improved by about 10%. Using these pitchnormalized phone models in an HMM-LR speech recognition system improved the phrase recognition accuracy for the top 5 candidates from 96% to 97.5%, i.e. the error rate was nearly halved.
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