HaikuS2S: A Cascaded System For Responding In Verse

Abstract:Expressive speech synthesis has advanced through prosody modeling, yet generating structured poetic speech, such as haiku, remains challenging. Prior work on prosody transfer improves expressiveness, and fine-tuned poetry TTS (text-to-speech) systems capture verse intonation. However, these models do not model haiku's 5-7-5 syllable structure or line-ending pauses. We present a cascaded system, HaikuS2S, combining ASR (automatic speech recognition), LLM (large language model)-generated haiku, and TTS fine-tuning on both prose and custom haiku datasets. Our evaluation focuses on emotion similarity, speech quality, and prosody alignment. In our experiments, we see that our prosody and tonal alignment improve significantly with our fine-tuned systems, particularly the one trained on both general poetry and haiku. We also see that we maintain similar emotion similarity scores across all systems.

Accepted to SLT 2026, Demo Track. 5 pages, 5 figures

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Sound (cs.SD)

arXiv:2609.23951 [cs.CL]

(or arXiv:2609.23951v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.23951 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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