Summary of Ser Evals: In-domain and Out-of-domain Benchmarking For Speech Emotion Recognition, by Mohamed Osman et al.
SER Evals: In-domain and Out-of-domain Benchmarking for Speech Emotion Recognition
by Mohamed Osman, Daniel Z. Kaplan, Tamer Nadeem
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a large-scale benchmark to evaluate the performance of speech emotion recognition (SER) models in diverse languages and emotional expressions. They aim to assess the robustness and adaptability of state-of-the-art SER models in both in-domain and out-of-domain settings. The proposed benchmark includes a range of multilingual datasets, focusing on less commonly used corpora to test generalization capabilities. Surprisingly, the Whisper model, designed for automatic speech recognition, outperforms dedicated SSL models in cross-lingual SER tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Speech emotion recognition (SER) is an important task that can help computers understand and recognize emotions from human speech. This paper proposes a new benchmark to test how well different SER models work on different languages and emotional expressions. The researchers used a variety of datasets from around the world and found that some models are better than others at recognizing emotions in different languages. |
Keywords
» Artificial intelligence » Generalization
