Papers › BERSting at the Screams: A Benchmark for Distanced, Emotional and Shouted Speech Recognition
BERSting at the Screams: A Benchmark for Distanced, Emotional and Shouted Speech Recognition
Paige Tuttösí, Mantaj Dhillon, Luna Sang, Shane Eastwood, Poorvi Bhatia, Quang Minh Dinh, Avni Kapoor, Yewon Jin, Angelica Lim
Some speech recognition tasks, such as automatic speech recognition (ASR), are approaching or have reached human performance in many reported metrics. Yet, they continue to struggle in complex, real-world, situations, such as with distanced speech. Previous challenges have released datasets to address the issue of distanced ASR, however, the focus remains primarily on distance, specifically relying on multi-microphone array systems. Here we present the B(asic) E(motion) R(andom phrase) S(hou)t(s) (BERSt) dataset. The dataset contains almost 4 hours of English speech from 98 actors with varying regional and non-native accents. The data was collected on smartphones in the actors homes and therefore includes at least 98 different acoustic environments. The data also includes 7 different emotion prompts and both shouted and spoken utterances. The smartphones were places in 19 different positions, including obstructions and being in a different room than the actor. This data is publicly available for use and can be used to evaluate a variety of speech recognition tasks, including: ASR, shout detection, and speech emotion recognition (SER). We provide initial benchmarks for ASR and SER tasks, and find that ASR degrades both with an increase in distance and shout level and shows varied performance depending on the intended emotion. Our results show that the BERSt dataset is challenging for both ASR and SER tasks and continued work is needed to improve the robustness of such systems for more accurate real-world use.
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Datasets
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Speech Emotion Recognition | BERSt | DAWN-hidden-SVM | Unweighted Accuracy (UA) | 32.1 | #1 of 3 | Archive leaderboard | report |
| Speech Emotion Recognition | BERSt | DAWN-hidden-SVM | Weighted Accuracy (WA) | 32.2 | #1 of 3 | Archive leaderboard | report |
| Speech Emotion Recognition | BERSt | Wav2Small-VAD-SVM | Unweighted Accuracy (UA) | 23.3 | #2 of 3 | Archive leaderboard | report |
| Speech Emotion Recognition | BERSt | Wav2Small-VAD-SVM | Weighted Accuracy (WA) | 22.3 | #2 of 3 | Archive leaderboard | report |
| Speech Emotion Recognition | BERSt | Speechbrain Wav2Vec2 | Unweighted Accuracy (UA) | 20.7 | #3 of 3 | Archive leaderboard | report |
| Speech Emotion Recognition | BERSt | Speechbrain Wav2Vec2 | Weighted Accuracy (WA) | 20.8 | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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