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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing;ICASSP >Speaker states recognition using latent factor analysis based Eigenchannel factor vector modeling
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Speaker states recognition using latent factor analysis based Eigenchannel factor vector modeling

机译：基于潜在因子分析的特征通道特征向量建模的说话人状态识别

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摘要

This paper presents an automatic speaker state recognition approach which models the factor vectors in the latent factor analysis framework improving upon the Gaussian Mixture Model (GMM) baseline performance. We investigate both intoxicated and affective speaker states. We consider the affective speech signal as the original normal average speech signal being corrupted by the affective channel effects. Rather than reducing the channel variability to enhance the robustness as in the speaker verification task, we directly model the speaker state on the channel factors under the factor analysis framework. In this work, the speaker state factor vectors are extracted and modeled by the latent factor analysis approach in the GMM modeling framework and support vector machine classification method. Experimental results show that the proposed speaker state factor vector modeling system achieved 5.34% and 1.49% unweighted accuracy improvement over the GMM baseline on the intoxicated speech detection task (Alcohol Language Corpus) and the emotion recognition task (IEMOCAP database), respectively.
机译：本文提出了一种自动的说话人状态识别方法，该方法在潜在因子分析框架中对因子向量进行建模，从而改善了高斯混合模型（GMM）的基线性能。我们调查着迷的说话者状态。我们认为情感语音信号是被情感通道效应破坏的原始正常平均语音信号。与其像说话者验证任务中那样减少声道可变性以增强鲁棒性，不如在因素分析框架下直接根据声道因素对说话者状态进行建模。在这项工作中，说话人状态因素向量是通过GMM建模框架中的潜在因素分析方法和支持向量机分类方法来提取和建模的。实验结果表明，所提出的说话人状态因子矢量建模系统在醉人语音检测任务（酒精语言语料库）和情感识别任务（IEMOCAP数据库）上分别比GMM基线提高了5.34％和1.49％的未加权准确性。

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