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

Abstract

Studies Japanese large-vocabulary continuous-speech recognition (LV CSR) for a Japanese business newspaper. To enable word N-grams to be used, sentences were first segmented into words (morphemes) using a morphological analyzer. About five years of newspaper articles were used to train N-gram language models. To evaluate our recognition system, we recorded speech data for sentences from another set of articles. Using the speech corpus, LV CSR experiments were conducted. For a 7k vocabulary, the word error rate was 82.8% when no grammar and context-independent acoustic models were used. This improved to 20.0% when both bigram language models and context-dependent acoustic models were used.