HumDial-EIBench is a new benchmark using real human dialogues to evaluate audio language models on emotional intelligence tasks including multi-turn tracking, causal reasoning, empathy generation, and acoustic-semantic conflict resolution.
OSUM: Advancing Open Speech Understanding Models with Limited Resources in Academia
6 Pith papers cite this work. Polarity classification is still indexing.
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MSU-Bench is a new two-tier benchmark covering speaker grounding to dialogue reasoning in multi-speaker conversations, with Gemini-assisted annotation and human verification.
Phoneme-level analysis of ASR on Archi and Rutul shows data scarcity explains recognition errors better than phonological complexity, with language-specific adaptations improving wav2vec2 performance.
Self-distillation of Qwen3-Omni-Thinking on 545k Cogito-Pipe audio reasoning traces yields the best open-source MMAR CoT scores and top-tier challenge ranking.
Introduces XLSR-Thai encoder, U-Align alignment, and Thai-SUP data pipeline to enable multitask speech understanding SLLMs for Thai.
Kimi-Audio is an open-source audio foundation model that achieves state-of-the-art results on speech recognition, audio understanding, question answering, and conversation after pre-training on more than 13 million hours of speech, sound, and music data.
citing papers explorer
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HumDial-EIBench: A Human-Recorded Multi-Turn Emotional Intelligence Benchmark for Audio Language Models
HumDial-EIBench is a new benchmark using real human dialogues to evaluate audio language models on emotional intelligence tasks including multi-turn tracking, causal reasoning, empathy generation, and acoustic-semantic conflict resolution.
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MSU-Bench: Towards Speaker-Centric Understanding in Conversational Multi-Speaker Scenarios
MSU-Bench is a new two-tier benchmark covering speaker grounding to dialogue reasoning in multi-speaker conversations, with Gemini-assisted annotation and human verification.
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Hard to Be Heard: Phoneme-Level ASR Analysis of Phonologically Complex, Low-Resource Endangered Languages
Phoneme-level analysis of ASR on Archi and Rutul shows data scarcity explains recognition errors better than phonological complexity, with language-specific adaptations improving wav2vec2 performance.
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Audio-Cogito: Towards Deep Audio Reasoning in Large Audio Language Models
Self-distillation of Qwen3-Omni-Thinking on 545k Cogito-Pipe audio reasoning traces yields the best open-source MMAR CoT scores and top-tier challenge ranking.
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Towards Building Speech Large Language Models for Multitask Understanding in Low-Resource Languages
Introduces XLSR-Thai encoder, U-Align alignment, and Thai-SUP data pipeline to enable multitask speech understanding SLLMs for Thai.
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Kimi-Audio Technical Report
Kimi-Audio is an open-source audio foundation model that achieves state-of-the-art results on speech recognition, audio understanding, question answering, and conversation after pre-training on more than 13 million hours of speech, sound, and music data.