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首页> 外文会议>European Conference on Speech Communication and Technology v.3; 20010903-20010907; Aalborg; DK >A Hybrid Approach to Enhance Task Portability of Acoustic Models in Chinese Speech Recognition
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A Hybrid Approach to Enhance Task Portability of Acoustic Models in Chinese Speech Recognition

机译：增强中文语音识别声学模型任务可移植性的混合方法

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摘要

This paper presents our approach to enhance the portability of acoustic models by mitigating the phonetic mismatch arising from a new testing task which is rather different from the training data. The approach is a hybrid one which combines knowledge-based context categorization to generate a context rich set of subword units, and data-driven-based acoustic model clustering on the level of context category. Compared with the conventional approach of only phonetic decision tree based model clustering and unseen model generation, the new approach improved greatly the desired subword coverage for the new testing domain, and achieved an error rate reduction by 10.8% for Chinese character accuracy in the recognition experiments. Together with the effect of the newly adopted basic units of 9 glottal stops, we achieved a total 23.5% error rate reduction in the testing compared to the baseline system.
机译：本文介绍了我们的方法，通过减轻与测试数据完全不同的新测试任务引起的语音不匹配，来增强声学模型的可移植性。该方法是一种混合方法，它结合了基于知识的上下文分类以生成上下文丰富的子词单元集，以及在上下文类别级别上基于数据驱动的声学模型聚类。与仅基于语音决策树的模型聚类和看不见的模型生成的常规方法相比，新方法极大地提高了新测试域所需的子词覆盖率，并在识别实验中将汉字精度的错误率降低了10.8％ 。加上新采用的9个声门止动的基本单位的效果，与基准系统相比，我们在测试中总共降低了23.5％的错误率。

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