Speaker Emotion Recognition: Leveraging Self-Supervised Models for Feature Extraction Using Wav2Vec2 and HuBERT

TL;DR

This work studies the use of self-supervised transformer-based models, Wav2Vec2 and HuBERT, to determine the emotion of speakers from their voice, and demonstrates that the model accurately predicts emotions.

Abstract

Speech is the most natural way of expressing ourselves as humans. Identifying emotion from speech is a nontrivial task due to the ambiguous definition of emotion itself. Speaker Emotion Recognition (SER) is essential for understanding human emotional behavior. The SER task is challenging due to the variety of speakers, background noise, complexity of emotions, and speaking styles. It has many applications in education, healthcare, customer service, and Human-Computer Interaction (HCI). Previously, conventional machine learning methods such as SVM, HMM, and KNN have been used for the SER task. In recent years, deep learning methods have become popular, with convolutional neural networks and recurrent neural networks being used for SER tasks. The input of these methods is mostly spectrograms and hand-crafted features. In this work, we study the use of self-supervised transformer-based models, Wav2Vec2 and HuBERT, to determine the emotion of speakers from their voice. The models automatically extract features from raw audio signals, which are then used for the classification task. The proposed solution is evaluated on reputable datasets, including RAVDESS, SHEMO, SAVEE, AESDD, and Emo-DB. The results show the effectiveness of the proposed method on different datasets. Moreover, the model has been used for real-world applications like call center conversations, and the results demonstrate that the model accurately predicts emotions.

Figures (3)

• Figure 1: The high-level description of our model. The input is a raw audio signal, in the second stage, features are extracted by transformer based model. Then, two feed forward layers classify the signal.
• Figure 2: Hubert model architecture
• Figure 3: Wav2Vec2.0 model architecture