Multi-layer attentive probing outperforms last-layer linear probing for transferring audio representations to bioacoustic tasks, indicating that standard evaluation setups may underestimate model quality.
Ml-superb 2.0: Benchmarking multilingual speech models across mod- eling constraints, languages, and datasets
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MUSCAT is a benchmark of bilingual scientific conversations designed to evaluate ASR systems on code-switching and domain-specific challenges.
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Multi-layer attentive probing improves transfer of audio representations for bioacoustics
Multi-layer attentive probing outperforms last-layer linear probing for transferring audio representations to bioacoustic tasks, indicating that standard evaluation setups may underestimate model quality.
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MUSCAT: MUltilingual, SCientific ConversATion Benchmark
MUSCAT is a benchmark of bilingual scientific conversations designed to evaluate ASR systems on code-switching and domain-specific challenges.