Large vocabulary word recognition based on tree-trellis search

In this paper we propose a large vocabulary (90000 words), Chinese (Mandarin) word recognizer based on the tree-trellis fast search algorithm. The recognizer is divided into 3 modules: local likelihood computation, a forward trellis search and a backward tree search. In the forward trellis search, a free syllable decoding is performed without a language model and a partial path map is created. The best-first tree search is then applied backward along a lexicon, which is arranged as a syllabic tree, to find the N-best word candidates. In the experiment, context-dependent subsyllabic HMMs were trained with a new discriminative training method. When it is evaluated on a speaker-trained database, the recognizer achieved a word error rate of 5% for the full size (90000 words) vocabulary and 1.7% for a smaller subset (5000 words) vocabulary. A real-time demo system has also been implemented on an SGI R-4000 workstation