Context-dependent hybrid HME/HMM speech recognition using polyphone clustering decision trees

This paper presents a context-dependent hybrid connectionist speech recognition system that uses a set of generalized hierarchical mixtures of experts (HME) to estimate context-dependent posterior acoustic class probabilities. The connectionist part of the system is organized in a modular fashion, allowing the distributed training of such a system on regular workstations. Context classes are based on polyphonic contexts, clustered using decision trees which we adopt from our continuous density HMM recognizer JANUS [8]. The system is evaluated on ESST, an english speaker-independent spontaneous speech database. Context dependent modeling is shown to yield signi cant improvements over simple context-independent modeling, requiring only small additional overhead in terms of training and decoding time.