汉语连续语音识别中不同基元声学模型的复合  

Combination of Acoustic Models Trained from Different Unit Sets for Chinese Continuous Speech Recognition

作　　者：张辉[1] 杜利民[1] 

机构地区：[1]中国科学院声学研究所语音交互技术实验室,北京100080

出　　处：《电子与信息学报》2006年第11期2045-2049,共5页Journal of Electronics & Information Technology

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%。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.

关 键 词：语音识别 声学模型复合 声学模型选择 错误率 

分 类 号：TN912.34[电子电信—通信与信息系统]