The Role of Phonetic Units in Speech Emotion Recognition

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ID: 282557
2021
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Abstract
We propose a method for emotion recognition through emotiondependent speech recognition using Wav2vec 2.0. Our method achieved a significant improvement over most previously reported results on IEMOCAP, a benchmark emotion dataset. Different types of phonetic units are employed and compared in terms of accuracy and robustness of emotion recognition within and across datasets and languages. Models of phonemes, broad phonetic classes, and syllables all significantly outperform the utterance model, demonstrating that phonetic units are helpful and should be incorporated in speech emotion recognition. The best performance is from using broad phonetic classes. Further research is needed to investigate the optimal set of broad phonetic classes for the task of emotion recognition. Finally, we found that Wav2vec 2.0 can be fine-tuned to recognize coarser-grained or larger phonetic units than phonemes, such as broad phonetic classes and syllables.
Reference Key
church2021the Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jiahong Yuan; Xingyu Cai; Renjie Zheng; Liang Huang; Kenneth Church
Journal arXiv
Year 2021
DOI
DOI not found
URL
http://arxiv.org/abs/2108.01132v1
Keywords
cs.cl

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