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.
InProceedings of the 22nd Interna- tional Conference on Spoken Language Transla- tion (IWSLT 2025), pages 315–323
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Introduces MUSCAT benchmark dataset of bilingual scientific discussions to evaluate multilingual ASR performance on code-switching and mixed inputs beyond standard WER.
Audio language models are benchmarked on five semantic and paralinguistic reasoning tasks to reveal limitations in handling spoken audio evidence, accent variation, and domain shifts.
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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
Introduces MUSCAT benchmark dataset of bilingual scientific discussions to evaluate multilingual ASR performance on code-switching and mixed inputs beyond standard WER.
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Afrispeech Semantics: Evaluating Audio Semantic Reasoning in Spoken Language Models Across Domains and Accents
Audio language models are benchmarked on five semantic and paralinguistic reasoning tasks to reveal limitations in handling spoken audio evidence, accent variation, and domain shifts.