汉语语音识别中基于音节的声学模型改进算法

Improved syllable-based acoustic modeling for continuous Chinese speech recognition

针对汉语语音识别中协同发音现象引起的语音信号的易变性,提出一种基于音节的声学建模方法。首先建立基于音节的声学模型以解决音节内部声韵母之间的音变现象,并提出以音节内双音子模型来初始化基于音节声学模型的参数以缓解训练数据稀疏的问题;然后引入音节之间的过渡模型来处理音节之间的协同发音问题。在"863-test"测试集上进行的汉语连续语音识别实验显示汉语字的相对错误率下降了12.13%,表明了基于音节的声学模型和音节间过渡模型相结合在解决汉语协同发音问题上的有效性。

Concerning the changeability of the speech signal caused by co-articulation phenomenon in Chinese speech recognition, a syllable-based acoustic modeling method was proposed. Firstly, context independent syllable-based acoustic models were trained, and the models were initialized by intra-syllable IFs based diphones to solve the problem of training data sparsity. Secondly, the inter-syllable co-articulation effect was captured by incorporating inter-syllable transition models into the recognition system. The experiments conducted on “863-test” dataset show that the relative character error rate is reduced by 12.13%. This proves that syUable-based acoustic model and inter-syllable transition model are effective in solving co- articulation effect.