The Role of Phonetic Units in Speech Emotion Recognition
This work addresses emotion recognition from speech, which is important for applications like human-computer interaction, but it is incremental as it builds on existing methods like Wav2vec 2.0.
The paper tackled speech emotion recognition by using emotion-dependent speech recognition with Wav2vec 2.0, achieving significant improvements on the IEMOCAP benchmark dataset, with broad phonetic classes yielding the best performance.
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.