Hello-Chat: Towards Realistic Social Audio Interactions
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917298600673280 |
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| author | Hou, Yueran Jia, Peilei Sun, Zihan Lu, Qihang Yang, Wenbing Gao, Yingming Li, Ya Gao, Jun |
| author_facet | Hou, Yueran Jia, Peilei Sun, Zihan Lu, Qihang Yang, Wenbing Gao, Yingming Li, Ya Gao, Jun |
| contents | Recent advancements in Large Audio Language Models (LALMs) have demonstrated exceptional performance in speech recognition and translation. However, existing models often suffer from a disconnect between perception and expression, resulting in a robotic "read-speech" style that lacks the spontaneity and emotional resonance of real human interaction. In this report, we introduce Hello-Chat, an end-to-end audio language model designed for realistic social scenarios. By leveraging a massive dataset of real-life conversations and employing a modality-interleaved training strategy, Hello-Chat achieves a breakthrough in anthropomorphic generation. Experimental results show that our model not only reaches state-of-the-art (SOTA) performance on specific audio understanding tasks but also significantly outperforms existing baselines in prosodic naturalness and emotional alignment, paving the way for the next generation of empathetic AI agents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_23387 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hello-Chat: Towards Realistic Social Audio Interactions Hou, Yueran Jia, Peilei Sun, Zihan Lu, Qihang Yang, Wenbing Gao, Yingming Li, Ya Gao, Jun Sound Artificial Intelligence Computation and Language Audio and Speech Processing Recent advancements in Large Audio Language Models (LALMs) have demonstrated exceptional performance in speech recognition and translation. However, existing models often suffer from a disconnect between perception and expression, resulting in a robotic "read-speech" style that lacks the spontaneity and emotional resonance of real human interaction. In this report, we introduce Hello-Chat, an end-to-end audio language model designed for realistic social scenarios. By leveraging a massive dataset of real-life conversations and employing a modality-interleaved training strategy, Hello-Chat achieves a breakthrough in anthropomorphic generation. Experimental results show that our model not only reaches state-of-the-art (SOTA) performance on specific audio understanding tasks but also significantly outperforms existing baselines in prosodic naturalness and emotional alignment, paving the way for the next generation of empathetic AI agents. |
| title | Hello-Chat: Towards Realistic Social Audio Interactions |
| topic | Sound Artificial Intelligence Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2602.23387 |