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首页> 外文期刊>IEEE signal processing letters >Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition
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Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition

机译：基于自动编码器的无监督域自适应语音情感识别

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摘要

With the availability of speech data obtained from different devices and varied acquisition conditions, we are often faced with scenarios, where the intrinsic discrepancy between the training and the test data has an adverse impact on affective speech analysis. To address this issue, this letter introduces an Adaptive Denoising Autoencoder based on an unsupervised domain adaptation method, where prior knowledge learned from a target set is used to regularize the training on a source set. Our goal is to achieve a matched feature space representation for the target and source sets while ensuring target domain knowledge transfer. The method has been successfully evaluated on the 2009 INTERSPEECH Emotion Challenge’s FAU Aibo Emotion Corpus as target corpus and two other publicly available speech emotion corpora as sources. The experimental results show that our method significantly improves over the baseline performance and outperforms related feature domain adaptation methods.
机译：随着从不同设备获得的语音数据的可用性和采集条件的变化，我们经常会遇到这样的场景：训练数据和测试数据之间的内在差异会对情感语音分析产生不利影响。为了解决这个问题，这封信介绍了一种基于无监督域自适应方法的自适应降噪自动编码器，其中从目标集学习的先验知识用于规范对源集的训练。我们的目标是在确保目标领域知识转移的同时，为目标集和源集实现匹配的特征空间表示。该方法已在2009年INTERSPEECH Emotion Challenge的FAU Aibo Emotion语料库作为目标语料库以及另外两个公开可用的语音情感语料库作为来源中得到了成功评估。实验结果表明，我们的方法大大提高了基线性能，并且优于相关的特征域自适应方法。

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