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Whisper-RIR-Mega: A Paired Clean-Reverberant Speech Benchmark for ASR Robustness to Room Acoustics

arXiv – CS AI|Mandip Goswami|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers introduce Whisper-RIR-Mega, a new benchmark dataset for testing automatic speech recognition robustness in reverberant acoustic environments. The study evaluates five Whisper models and finds that reverberation consistently degrades performance across all model sizes, with word error rates increasing by 0.12 to 1.07 percentage points.

Key Takeaways

→Whisper-RIR-Mega pairs clean LibriSpeech utterances with reverberant versions using real room impulse responses for ASR testing.
→All five tested Whisper models (tiny through large-v3) showed performance degradation in reverberant conditions.
→Reverberation penalty in word error rate ranged from 0.12 to 1.07 percentage points depending on model size.
→The dataset includes stratified splits by reverberation time and direct-to-reverberant ratio for comprehensive evaluation.
→Researchers released the dataset, evaluation code, and baseline results to support reproducible ASR research.

#whisper #asr #speech-recognition #acoustic-robustness #benchmark-dataset #machine-learning #audio-processing #research

Read Original →via arXiv – CS AI

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