Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognition
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.
Cite
@article{arxiv.2110.06309,
title = {Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognition},
author = {Li-Wei Chen and Alexander Rudnicky},
journal= {arXiv preprint arXiv:2110.06309},
year = {2023}
}
Comments
Accepted to ICASSP 2023