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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2603.12565 |
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| _version_ | 1866915858638438400 |
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| author | Zhao, Mengjie Liu, Lianbo Fujita, Yusuke Shi, Hao Gao, Yuan Koshkin, Roman Sudo, Yui |
| author_facet | Zhao, Mengjie Liu, Lianbo Fujita, Yusuke Shi, Hao Gao, Yuan Koshkin, Roman Sudo, Yui |
| contents | SpeechLLMs typically combine ASR-trained encoders with text-based LLM backbones, leading them to inherit written-style output patterns unsuitable for text-to-speech synthesis. This mismatch is particularly pronounced in Japanese, where spoken and written registers differ substantially in politeness markers, sentence-final particles, and syntactic complexity. We propose a preference-based alignment approach to adapt Japanese SpeechLLMs for speech-worthy outputs: text that is concise, conversational, and readily synthesized as natural speech. To rigorously evaluate this task, we introduce SpokenElyza, a benchmark for Japanese speech-worthiness derived from ELYZA-tasks-100 with auditory verification by native experts. Experiments show that our approach achieves substantial improvement on SpokenElyza while largely preserving performance on the original written-style evaluation. We will release SpokenElyza to support future research on Japanese spoken dialog systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12565 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Speech-Worthy Alignment for Japanese SpeechLLMs via Direct Preference Optimization Zhao, Mengjie Liu, Lianbo Fujita, Yusuke Shi, Hao Gao, Yuan Koshkin, Roman Sudo, Yui Sound Computation and Language SpeechLLMs typically combine ASR-trained encoders with text-based LLM backbones, leading them to inherit written-style output patterns unsuitable for text-to-speech synthesis. This mismatch is particularly pronounced in Japanese, where spoken and written registers differ substantially in politeness markers, sentence-final particles, and syntactic complexity. We propose a preference-based alignment approach to adapt Japanese SpeechLLMs for speech-worthy outputs: text that is concise, conversational, and readily synthesized as natural speech. To rigorously evaluate this task, we introduce SpokenElyza, a benchmark for Japanese speech-worthiness derived from ELYZA-tasks-100 with auditory verification by native experts. Experiments show that our approach achieves substantial improvement on SpokenElyza while largely preserving performance on the original written-style evaluation. We will release SpokenElyza to support future research on Japanese spoken dialog systems. |
| title | Speech-Worthy Alignment for Japanese SpeechLLMs via Direct Preference Optimization |
| topic | Sound Computation and Language |
| url | https://arxiv.org/abs/2603.12565 |