AI RESEARCH
Speech-Worthy Alignment for Japanese SpeechLLMs via Direct Preference Optimization
arXiv CS.CL
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ArXi:2603.12565v1 Announce Type: cross 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.