漢語連續(xù)語音識別中不同基元聲學模型的復合
Combination of Acoustic Models Trained from Different Unit Sets for Chinese Continuous Speech Recognition
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摘要: 該文研究由不同聲學基元訓練的聲學模型的復合。在漢語連續(xù)語音識別中，流行的基元包括上下文相關的聲韻母基元和音素基元。實驗發(fā)現(xiàn)，有些漢語音節(jié)在聲韻母模型下有更高的識別率，有些音節(jié)在音素模型下有更高的識別率。該文提出一種復合這兩種聲學模型的方法，一方面在識別過程中同時使用兩種模型，另一方面在識別過程中避開造成低識別率的模型。實驗表明，采用本文的方法后，音節(jié)錯誤率比音素模型和聲韻母模型分別下降了9.60%和6.10%。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.
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