Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognition

Understand

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.