Japanese large-vocabulary continuous-speech recognition using a business-newspaper corpus

Abstract

A large-vocabulary continuous-speech recognition (LVCSR) system was developed and evaluated. To evaluate the system, a Japanese business-newspaper speech corpus was designed and recorded. The corpus was designed so that is can be used for Japanese LVCSR research in the same way that the Wall Street Journal (WSJ) corpus, for example, is used for English LVCSR research. Since Japanese sentences are written without spaces between words, a morphological analysis was introduced to segment sentences into words so that word n-gram language models could be used. To enable the use of detailed word n-gram (n/spl ges/3) language models, a two-pass decoding strategy was applied. Context-dependent (CD) phone models and word trigram language models reduced the word error rate from 80.2% to 10.1% (an error reduction of about 88%). This result shows that CD phoneme modeling and word trigram language models can be used effectively in Japanese LVCSR.