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Speech Unit Category based Short Utterance Speaker Recognition

机译：基于语音单元类别的简短讲话者识别

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摘要

Information of speech units like vowels, consonants and syllables can be a kind of knowledge used in text-independent Short Utterance Speaker Recognition (SUSR) in a similar way as in text-dependent speaker recognition. In such tasks, data for each speech unit, especially at the time of recognition, is often not enough. Hence, it is not practical to use the full set of speech units because some of the units might not be well trained. To solve this problem, a method of using speech unit categories rather than individual phones is proposed for SUSR, wherein similar speech units are put together, hence solving the problem of sparse data. We define Vowel, Consonant, and Syllable Categories (VC, CC and SC) with Standard Chinese (Putonghua) as a reference. A speech utterance is recognized into VC, CC ad SC sequences which are used to train Universal Background Models (UBM) for each speech unit category in the training procedure, and to perform speech unit category dependent speaker recognition, respectively. Experimental results in Gaussian Mixture Model-Universal Background Model (GMM-UBM) based system give a relative equal error rate (EER) reduction of 54.50% and 40.95% from minimum EERs of VCs and SCs, respectively, for 2 seconds of test utterance compared with the existing SUSR systems.
机译：元音，辅音和音节之类的语音单元信息可以是与文本无关的说话者语音识别（SUSR）中使用的一种知识，其方式类似于与文本相关的说话者语音识别。在这样的任务中，每个语音单元的数据，尤其是在识别时，通常是不够的。因此，使用整套语音单元是不切实际的，因为某些单元可能没有得到很好的训练。为了解决该问题，提出了一种针对SUSR使用语音单位类别而不是单个电话的方法，其中将相似的语音单位放在一起，从而解决了数据稀疏的问题。我们以标准中文（普通话）为参考定义元音，辅音和音节类别（VC，CC和SC）。语音发声被识别为VC，CC和SC序列，分别用于训练过程中每个语音单元类别的通用背景模型（UBM）的训练以及与语音单元类别相关的说话者识别。在基于高斯混合模型-通用背景模型（GMM-UBM）的系统中的实验结果表明，相比2秒的测试发声，VC和SC的最小EER分别相对降低了54.50％和40.95％的均等错误率（EER）与现有的SUSR系统。

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