Analysis and recognition of spontaneous speech using Corpus of Spontaneous Japanese

Abstract

Although speech is in almost any situation spontaneous, recognition of spontaneous speech is an area which has only recently emerged in the field of automatic speech recognition. Broadening the application of speech recognition depends crucially on raising recognition performance for spontaneous speech. For this purpose, it is necessary to analyze and model spontaneous speech using spontaneous speech databases, since spontaneous speech and read speech are significantly different. This paper reports analysis and recognition of spontaneous speech using a large-scale spontaneous speech database “Corpus of Spontaneous Japanese (CSJ)”. Recognition results in this experiment show that recognition accuracy significantly increases as a function of the size of acoustic as well as language model training data and the improvement levels off at approximately 7M words of training data. This means that acoustic and linguistic variation of spontaneous speech is so large that we need a very large corpus in order to encompass the variations. Spectral analysis using various styles of utterances in the CSJ shows that the spectral distribution/difference of phonemes is significantly reduced in spontaneous speech compared to read speech. It has also been observed that speaking rates of both vowels and consonants in spontaneous speech are significantly faster than those in read speech.