A new acoustic modeling of inter-syllable context-dependent units for Putonghua continuous speech recognition

To capture the coarticulatory effects in Putonghua continuous speech is important to improve the performance of automatic speech recognition system. A new acoustic modeling technique to construct inter-syllable context-dependent units is proposed, which is based on some particular characteristics of Putonghua. The acoustic model is detailed and context-dependent units are formed after phonetic coarticulation between neighboring syllables is taken into account. Then various contextual influences between syllables are classified based on Putonghua phonetic knowledge. This phonetic classification makes sharing parameters across different units possible, which can significantly reduce the complexity of acoustic model and construct a trainable model. Compared with traditional parameter-sharing techniques, this one is purely based on phonetics, instead of acoustical data-driven clustering. Experimental results show that this technique can significantly improve system performance. The proposed method reduces error rate by 17%.