Continuous speech recognition by context-dependent phonetic HMM and an efficient algorithm for finding N-Best sentence hypotheses

A continuous speech recognition system `niNja´ (Natural language INterface in JApanese), is presented. Efficient search algorithms are proposed to get high accuracy and to reduce the required computations. First, an LR parsing algorithm with context-dependent phone models is proposed. Second, scores of the same phone models in different hypotheses at the phone-level are represented by the single score of the best hypotheses. The system is tested for the task with a 113 word vocabulary, with a word perplexity of 4.1. It produces a sentence accuracy of 97.3% for the 10 open speakers´ 110 sentences and the error reduction is as much as 77% compared with using context independent phone models