%0 Journal Article
%T Combination of Acoustic Models Trained from Different Unit Sets for Chinese Continuous Speech Recognition<br>汉语连续语音识别中不同基元声学模型的复合
%A Zhang Hui
%A Du Li-min
%A <br>张 辉
%A 杜利民
%J 电子与信息学报
%D 2006
%I 
%X 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.
%K Speech recognition
%K Acoustic model combination
%K Acoustic model selection
%K Error rate<br>语音识别
%K 声学模型复合
%K 声学模型选择
%K 错误率
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=47D0DE4C5769A3D7&yid=37904DC365DD7266&vid=D3E34374A0D77D7F&iid=708DD6B15D2464E8&sid=28A5659E3BD66D6A&eid=53A9347E622951C1&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=1&reference_num=7