Multi-Microphone Speech Emotion Recognition using the Hierarchical Token-semantic Audio Transformer Architecture

• URL: http://arxiv.org/abs/2406.03272v3
• Date: Sat, 14 Sep 2024 20:46:25 GMT
• Title: Multi-Microphone Speech Emotion Recognition using the Hierarchical Token-semantic Audio Transformer Architecture
• Authors: Ohad Cohen, Gershon Hazan, Sharon Gannot,
• Abstract summary: 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. Our multi-microphone model achieves superior performance compared to single-channel baselines when tested on real-world reverberant environments.
• Score: 11.063156506583562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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