Vowel-category based Short Utterance Speaker Recognition

2012 International Conference on Systems and Informatics (ICSAI2012) Pub Date : 2012-05-19 DOI:10.1109/ICSAI.2012.6223387

N. Fatima, T. Zheng

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引用次数: 12

Abstract

The impact of Short Utterances in Speaker Recognition is of significant importance. Despite the advancements in short utterance speaker recognition (SUSR), text dependence and the role of phonemes in carrying speaker information needs further investigation. This paper presents a novel method of using vowel categories for SUSR. We define Vowel Categories (VC's) considering Chinese and English languages. After recognition and extraction of phonemes, the obtained vowels are divided into VC's, which are then used to develop Universal Background VC Models (UBVCM) for each VC. Conventional GMM-UBM system is used for training and testing. The proposed categories give minimum EERs of 13.76%, 14.03% and 16.18% for 3, 2 and 1 second respectively. Experimental results show that in text dependent SUSR, significant speaker-specific information is present at phoneme level. The similar properties of phonemes can be used such that accurate speech recognition is not required, rather Phoneme Categories can be used effectively for SUSR. Also, it is shown that vowels contain large amount of speaker information, which remains undisturbed when VC are employed.

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基于元音分类的短话语说话人识别

短话语在说话人识别中的作用是非常重要的。尽管短话语说话人识别(SUSR)技术取得了一定的进步，但文本依赖和音素承载说话人信息的作用还有待进一步研究。本文提出了一种利用元音类别进行超语音识别的新方法。我们根据汉语和英语两种语言来定义元音类别。在对音素进行识别和提取后，将得到的元音分成若干个VC，然后利用这些VC建立每个VC的通用背景VC模型(Universal Background VC model, UBVCM)。常规GMM-UBM系统用于培训和测试。建议的类别在3秒、2秒和1秒内的最低EERs分别为13.76%、14.03%和16.18%。实验结果表明，在文本依赖的SUSR中，在音素水平上存在显著的说话人特定信息。音素的相似属性可以使用，这样就不需要精确的语音识别，而音素类别可以有效地用于SUSR。同时，元音包含大量的说话人信息，当使用VC时，这些信息不受干扰。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2012 International Conference on Systems and Informatics (ICSAI2012)

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