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首页> 外文期刊>Engineering Applications of Artificial Intelligence >Multistage data selection-based unsupervised speaker adaptation for personalized speech emotion recognition
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Multistage data selection-based unsupervised speaker adaptation for personalized speech emotion recognition

机译：基于多阶段数据选择的无监督说话者自适应，用于个性化语音情感识别

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摘要

This paper proposes an efficient speech emotion recognition (SER) approach that utilizes personal voice data accumulated on personal devices. A representative weakness of conventional SER systems is the user-dependent performance induced by the speaker independent (SI) acoustic model framework. But, handheld communications devices such as smartphones provide a collection of individual voice data, thus providing suitable conditions for personalized SER that is more enhanced than the SI model framework. By taking advantage of personal devices, we propose an efficient personalized SER scheme employing maximum likelihood linear regression (MLLR), a representative speaker adaptation technique. To further advance the conventional MLLR technique for SER tasks, the proposed approach selects useful data that convey emotionally discriminative acoustic characteristics and uses only those data for adaptation. For reliable data selection, we conduct multistage selection using a log-likelihood distance-based measure and a universal background model. On SER experiments based on a Linguistic Data Consortium emotional speech corpus, our approach exhibited superior performance when compared to conventional adaptation techniques as well as the SI model framework.
机译：本文提出了一种有效的语音情感识别（SER）方法，该方法利用了在个人设备上累积的个人语音数据。常规SER系统的一个典型弱点是由独立于扬声器的（SI）声学模型框架引起的用户相关性能。但是，诸如智能手机之类的手持通信设备提供了单个语音数据的集合，因此为个性化SER提供了比SI模型框架更加增强的合适条件。通过利用个人设备，我们提出了一种有效的个性化SER方案，该方案采用了最大似然线性回归（MLLR）（一种有代表性的说话者自适应技术）。为了进一步推进用于SER任务的常规MLLR技术，所提出的方法选择了传达情感上有区别的声学特性的有用数据，并且仅将这些数据用于自适应。为了进行可靠的数据选择，我们使用基于对数似然距离的度量和通用背景模型进行多阶段选择。在基于语言数据联盟情感语音语料库的SER实验中，与传统的适应技术以及SI模型框架相比，我们的方法表现出了卓越的性能。

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