Papers by Emiru Tsunoo
Optimizing Conversational Quality in Spoken Dialogue Systems with Reinforcement Learning from AI Feedback (2026.findings-acl)
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Siddhant Arora, Jinchuan Tian, Jiatong Shi, Hayato Futami, Yosuke Kashiwagi, Emiru Tsunoo, Shinji Watanabe
| Challenge: | Existing studies on reinforcement learning from human or AI feedback have focused on semantic rewards at the utterance level. |
| Approach: | They propose a multi-reward RLAIF framework for speech-in/speech-out dialogue systems . they combine semantic, audio-quality, and emotion-consistency rewards . |
| Outcome: | The proposed framework improves speech-in/speech-out dialogue system quality . it combines semantic, audio-quality, and emotion-consistency rewards . the proposed framework is available to download from the cdc. |
UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions (2024.naacl-long)
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Siddhant Arora, Hayato Futami, Jee-weon Jung, Yifan Peng, Roshan Sharma, Yosuke Kashiwagi, Emiru Tsunoo, Karen Livescu, Shinji Watanabe
| Challenge: | Recent studies leverage large language models with multi-tasking capabilities, using natural language prompts to guide the model’s behavior and surpassing performance of task-specific models. |
| Approach: | They adapt a pre-trained automatic speech recognition model to additional tasks using single-token task specifiers. |
| Outcome: | The proposed model can generalize to new datasets and languages for seen task types. |