Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognition

Li-Wei Chen; Alexander Rudnicky

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arXiv · 2110.06309

Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognition

Li-Wei Chen·Alexander Rudnicky

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Abstract

While Wav2Vec 2.0 has been proposed for speech recognition (ASR), it can also be used for speech emotion recognition (SER); its performance can be significantly improved using different fine-tuning strategies. Two baseline methods, vanilla fine-tuning (V-FT) and task adaptive pretraining (TAPT) are first presented. We show that V-FT is able to outperform state-of-the-art models on the IEMOCAP dataset. TAPT, an existing NLP fine-tuning strategy, further improves the performance on SER. We also introduce a novel fine-tuning method termed P-TAPT, which modifies the TAPT objective to learn contextualized emotion representations. Experiments show that P-TAPT performs better than TAPT, especially under low-resource settings. Compared to prior works in this literature, our top-line system achieved a 7.4\% absolute improvement in unweighted accuracy (UA) over the state-of-the-art performance on IEMOCAP. Our code is publicly available.

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BibTeX RIS

Li-Wei Chen, Alexander Rudnicky. 2021-10-12. Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognition. https://arxiv.org/abs/2110.06309

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