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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >Domain-Adversarial Autoencoder with Attention Based Feature Level Fusion for Speech Emotion Recognition
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Domain-Adversarial Autoencoder with Attention Based Feature Level Fusion for Speech Emotion Recognition

机译：基于域 - 普发的AutoEncoder基于注意力的特征级融合，用于语音情感识别

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摘要

Over the past two decades, although speech emotion recognition (SER) has garnered considerable attention, the problem of insufficient training data has been unresolved. A potential solution for this problem is to pre-train a model and transfer knowledge from large amounts of audio data. However, the data used for pre-training and testing originate from different domains, resulting in the latent representations to contain non-affective information. In this paper, we propose a domain-adversarial autoencoder to extract discriminative representations for SER. Through domain-adversarial learning, we can reduce the mismatch between domains while retaining discriminative information for emotion recognition. We also introduce multi-head attention to capture emotion information from different subspaces of input utterances. Experiments on IEMOCAP show that the proposed model outperforms the state-of-the-art systems by improving the unweighted accuracy by 4.15%, thereby demonstrating the effectiveness of the proposed model.
机译：在过去的二十年中，虽然语音情感认可（SER）已经获得了相当大的关注，但训练数据不足的问题已经过分解决。该问题的潜在解决方案是预先训练模型并从大量音频数据传输知识。但是，用于预训练和测试的数据源自不同的域，导致潜在的表示包含非情感信息。在本文中，我们提出了一个域 - 普遍的AutoEncoder，以提取SER的鉴别表现。通过域 - 对抗的学习，我们可以减少域之间的不匹配，同时保留情感识别的判别信息。我们还介绍了多主题注意，以捕捉来自输入话语的不同子空间的情绪信息。 IEMocap实验表明，所提出的模型通过提高了4.15％，通过提高了415％，表明了所提出的模型的有效性，这促进了最先进的系统。

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