基于听者依赖情绪感知模型的语音情绪识别

Atsushi Ando, Takeshi Mori, Satoshi Kobashikawa, T. Toda
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引用次数: 3

摘要

本文提出了一种利用情感感知个性的语音情感识别方案。大多数传统方法只是简单地对多个听众进行投票,并直接将多数人的决定建模为感知到的情绪。然而,情感感知随听者的不同而变化,这迫使传统方法的单一模型创建复杂的情感感知标准混合物。为了缓解这一问题,我们提出了一个多数投票的情绪识别框架,该框架构建了听众依赖(LD)情绪识别模型。LD模型不仅可以估计听众感知情绪,还可以通过平均多个LD模型的输出来估计多数决策。引入了微调、辅助输入和子层加权三种LD模型,它们都受到了各种语音处理任务中成功的领域自适应框架的启发。在两个情感语音数据集上的实验表明,该方法不仅在多数投票方面优于传统的情感识别框架,而且在听众感知情感识别方面优于传统的情感识别框架。
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Speech emotion recognition based on listener-dependent emotion perception models
This paper presents a novel speech emotion recognition scheme that leverages the individuality of emotion perception. Most conventional methods simply poll multiple listeners and directly model the majority decision as the perceived emotion. However, emotion perception varies with the listener, which forces the conventional methods with their single models to create complex mixtures of emotion perception criteria. In order to mitigate this problem, we propose a majority-voted emotion recognition framework that constructs listener-dependent (LD) emotion recognition models. The LD model can estimate not only listener-wise perceived emotion, but also majority decision by averaging the outputs of the multiple LD models. Three LD models, fine-tuning, auxiliary input, and sub-layer weighting, are introduced, all of which are inspired by successful domain-adaptation frameworks in various speech processing tasks. Experiments on two emotional speech datasets demonstrate that the proposed approach outperforms the conventional emotion recognition frameworks in not only majority-voted but also listener-wise perceived emotion recognition.
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来源期刊
APSIPA Transactions on Signal and Information Processing
APSIPA Transactions on Signal and Information Processing ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
8.60
自引率
6.20%
发文量
30
审稿时长
40 weeks
期刊最新文献
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