汉语连续语音识别中不同基元声学模型的复合-电子与信息学报.pdfVIP

第28 卷第 11 期 电 子 与 信 息 学 报 Vol.28No.11 2006 年 11 月 Journal of Electronics Information Technology Nov.2006 汉语连续语音识别中不同基元声学模型的复合 张 辉 杜利民 ( 中国科学院声学研究所语音交互技术实验室 北京 100080) 摘 要 该文研究由不同声学基元训练的声学模型的复合。在汉语连续语音识别中，流行的基元包括上下文相关的 声韵母基元和音素基元。实验发现，有些汉语音节在声韵母模型下有更高的识别率，有些音节在音素模型下有更高 的识别率。该文提出一种复合这两种声学模型的方法，一方面在识别过程中同时使用两种模型，另一方面在识别过 程中避开造成低识别率的模型。实验表明，采用本文的方法后，音节错误率比音素模型和声韵母模型分别下降了 9.60%和 6.10% 。 关键词 语音识别，声学模型复合，声学模型选择，错误率 中图分类号：TP391.42 文献标识码：A 文章编号：1009-5896(2006)11-2045-05 Combination of Acoustic Models Trained from Different Unit Sets for Chinese Continuous Speech Recognition Zhang Hui Du Li-min (SITR, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100080, China) Abstract Combination of acoustic models trained from different unit sets is studied in this paper. For Chinese continuous speech recognition, Prevailing unit sets include context-dependent initial-final unit set and context-dependent phone unit set. Through experiments it is discovered that some Chinese syllables have higher recognition rates under initial-final model while some have higher recognition rates under phone model. In this paper, a method is proposed to combine these two acoustic models. On one hand the two acoustic models can be fully utilized during the recognition process; on the other hand, some models that lead to low recognition rate will not be used. Experiments show that in comparison with initial-final model and phone model, syllable error rate is reduced by 9.60% and 6.10% respectively after using the provided method. Key words Sp