Multi-Microphone Speech Emotion Recognition using the Hierarchical Token-semantic Audio Transformer Architecture
Abstract
The performance of most emotion recognition systems degrades in real-life situations ('in the wild' scenarios) where the audio is contaminated by reverberation. Our study explores new methods to alleviate the performance degradation of SER algorithms and develop a more robust system for adverse conditions. We propose processing multi-microphone signals to address these challenges and improve emotion classification accuracy. We adopt a state-of-the-art transformer model, the HTS-AT, to handle multi-channel audio inputs. We evaluate two strategies: averaging mel-spectrograms across channels and summing patch-embedded representations. Our multi-microphone model achieves superior performance compared to single-channel baselines when tested on real-world reverberant environments.
Cite
@article{arxiv.2406.03272,
title = {Multi-Microphone Speech Emotion Recognition using the Hierarchical Token-semantic Audio Transformer Architecture},
author = {Ohad Cohen and Gershon Hazan and Sharon Gannot},
journal= {arXiv preprint arXiv:2406.03272},
year = {2024}
}