Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings

• URL: http://arxiv.org/abs/2104.03502v1
• Date: Thu, 8 Apr 2021 04:31:58 GMT
• Title: Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings
• Authors: Leonardo Pepino, Pablo Riera, Luciana Ferrer
• Abstract summary: We propose a transfer learning method for speech emotion recognition. We combine the output of several layers from the pre-trained model using trainable weights which are learned jointly with the downstream model. We evaluate our proposed approaches on two standard emotion databases IEMOCAP and RAVDESS, showing superior performance compared to results in the literature.
• Score: 16.829474982595837
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Emotion recognition datasets are relatively small, making the use of the more sophisticated deep learning approaches challenging. In this work, we propose a transfer learning method for speech emotion recognition where features extracted from pre-trained wav2vec 2.0 models are modeled using simple neural networks. We propose to combine the output of several layers from the pre-trained model using trainable weights which are learned jointly with the downstream model. Further, we compare performance using two different wav2vec 2.0 models, with and without finetuning for speech recognition. We evaluate our proposed approaches on two standard emotion databases IEMOCAP and RAVDESS, showing superior performance compared to results in the literature.

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