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首页> 外文期刊>WSEAS Transactions on Signal Processing >Speaker verification using speaker-specific-text
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Speaker verification using speaker-specific-text

机译：使用说话人特定文本进行说话人验证

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摘要

In speaker recognition tasks, one of the reasons for reduced accuracy is due to closely resembling speakers in the acoustic space. In conventional GMM-based modeling technique, since the model parameters of a class are estimated without considering other classes in the system, features that are common across various classes may also be captured, along with unique features. If the system is designed to use only the unique features of a given speaker with respect to his/her acoustically resembling speaker, then the system is expected to perform better. In this proposed work, the effect of a subset of phonemes, reasonably distinct (unique) to a speaker, in the acoustic sense, on a speaker verification task is investigated. This paper proposes a technique to reduce the confusion errors, by finding speaker-specific phonemes and formulate a text using the subset of phonemes that are unique, for speaker verification task using GMM-based approach and i-vector based approach. We have experimented with three techniques namely, product of likelihood-Gaussians-based distance, Bhattacharyya distance and average log-likelihood-based distance to find out acoustically unique phonemes. Experiments have been conducted on speaker verification task using speech data of 50 speakers collected in a laboratory environment. The experiments show that the Equal Error Rate (EER) has been decreased by 4% and 4.5% using speaker-specific-text when compared to that of GMM and i-vector technique with random-text respectively.
机译：在说话人识别任务中，准确性降低的原因之一是由于声音空间中的说话人极为相似。在传统的基于GMM的建模技术中，由于在不考虑系统中其他类别的情况下估计了类别的模型参数，因此也可以捕获各个类别之间共有的特征以及独特特征。如果系统被设计为仅使用给定扬声器相对于他/她的声学相似扬声器的独特功能，则系统有望表现更好。在这项拟议的工作中，研究了在语音意义上对说话者来说合理区分（唯一）的音素子集对说话者验证任务的影响。本文针对基于GMM和基于i-vector的说话人验证任务，提出了一种通过查找说话人特定音素并使用唯一音素子集来编写文本来减少混淆错误的技术。我们已经尝试了三种技术，即基于似然-高斯的距离，Bhattacharyya距离和基于平均对数似然的距离的乘积，以找出声学上唯一的音素。已经使用在实验室环境中收集的50位演讲者的语音数据对演讲者验证任务进行了实验。实验表明，与GMM和带有随机文本的i-vector技术相比，使用说话人特定文本的均等错误率（EER）分别降低了4％和4.5％。

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