Voice signatures
Voice signatures
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DOI：
10.1109/asru.2003.1318399
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发表时间：
2003
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影响因子：
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通讯作者：
Izhak Shafran;Michael Riley;Mehryar Mohri
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文献类型：
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作者：
Izhak Shafran;Michael Riley;Mehryar Mohri
大多数当前的口语对话系统仅从说话者的声音中提取单词序列。这在很大程度上忽略了可以从语音中推断出的其他有用信息，例如性别、年龄、方言或情感。说话者的语音、语音签名的这些特征，无论是静态的还是动态的，对于语音挖掘应用程序或自然口语对话系统的设计都是有用的。本文探讨了从说话者的声音中自动准确地提取语音签名的问题。我们研究了两种提取说话者特征的方法：第一种侧重于一般声学和韵律特征，第二种侧重于说话者使用的单词的选择。在第一种方法中，我们表明，以说话者特征为条件并根据倒谱和音调特征进行评估的标准语音/非语音 HMM 所达到的准确度远高于所有检查特征的机会。第二种方法使用具有理性核的支持向量机应用于语音识别格，在情感二元分类任务中获得了约 8.1% 的准确率。我们的结果基于从已部署的客户服务应用程序 (HMIHY 0300) 收集的语音数据集。虽然仍处于初步阶段，但我们的结果很重要，并且表明语音签名在实际应用中具有实际意义。
Most current spoken-dialog systems only extract sequences of words from a speaker's voice. This largely ignores other useful information that can be inferred from speech such as gender, age, dialect, or emotion. These characteristics of a speaker's voice, voice signatures, whether static or dynamic, can be useful for speech mining applications or for the design of a natural spoken-dialog system. This paper explores the problem of extracting automatically and accurately voice signatures from a speaker's voice. We investigate two approaches for extracting speaker traits: the first focuses on general acoustic and prosodic features, the second on the choice of words used by the speaker. In the first approach, we show that standard speech/nonspeech HMM, conditioned on speaker traits and evaluated on cepstral and pitch features, achieve accuracies well above chance for all examined traits. The second approach, using support vector machines with rational kernels applied to speech recognition lattices, attains an accuracy of about 8.1 % in the task of binary classification of emotion. Our results are based on a corpus of speech data collected from a deployed customer-care application (HMIHY 0300). While still preliminary, our results are significant and show that voice signatures are of practical interest in real-world applications.