SER Evals: In-domain and Out-of-domain Benchmarking for Speech Emotion Recognition

• URL: http://arxiv.org/abs/2408.07851v1
• Date: Wed, 14 Aug 2024 23:33:10 GMT
• Title: SER Evals: In-domain and Out-of-domain Benchmarking for Speech Emotion Recognition
• Authors: Mohamed Osman, Daniel Z. Kaplan, Tamer Nadeem,
• Abstract summary: Speech emotion recognition (SER) has made significant strides with the advent of powerful self-supervised learning (SSL) models. We propose a large-scale benchmark to evaluate the robustness and adaptability of state-of-the-art SER models. We find that the Whisper model, primarily designed for automatic speech recognition, outperforms dedicated SSL models in cross-lingual SER.
• Score: 3.4355593397388597
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Speech emotion recognition (SER) has made significant strides with the advent of powerful self-supervised learning (SSL) models. However, the generalization of these models to diverse languages and emotional expressions remains a challenge. We propose a large-scale benchmark to evaluate the robustness and adaptability of state-of-the-art SER models in both in-domain and out-of-domain settings. Our benchmark includes a diverse set of multilingual datasets, focusing on less commonly used corpora to assess generalization to new data. We employ logit adjustment to account for varying class distributions and establish a single dataset cluster for systematic evaluation. Surprisingly, we find that the Whisper model, primarily designed for automatic speech recognition, outperforms dedicated SSL models in cross-lingual SER. Our results highlight the need for more robust and generalizable SER models, and our benchmark serves as a valuable resource to drive future research in this direction.

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