Continuous speech recognition by context-dependent phonetic HMM and an efficient algorithm for finding N-Best sentence hypotheses
I. Katunobu, H. Satoru, Takeshi Hozumi · 1992
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.>