HMM-based phonemic distance in different speaking styles and its influence on substitutions in Mandarin speech recognition

Statistical confusability between different acoustic models is important to character substitution error rate in large vocabulary continuous speech recognition. In this paper, we take factors of gender and speaking styles into consideration in Mandarin speech recognition. We modeled phonemes in different speaking styles, including read speech of female, male, and spontaneous dialogue. Then minimum gaussian distances between Chinese Initial/Final model pairs are given and average phoneme distances are calculated which denote the pronunciation varieties. The effect of different style to average phonemic distance is studied and relative articulation is given for three databases. Qualitative relationship between phone size and error rate in recognition is analytical researched, showing that for a particular phoneme, pronunciation variety is one of reasons for misidentification in recognizing process, which provides us a novel mind to reduce substitution errors.