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This research introduces ReACT-TTS, a framework that leverages one-second pre-response facial cues from listeners to optimize the emotion and prosody of conversational speech generation. By integrating visual dynamics into the planning stage before realization, the model achieves superior concordance in dialogue protocols compared to text-only approaches without sacrificing accuracy. The findings suggest that incorporating immediate listener feedback is a viable strategy for enhancing the naturalness and contextual appropriateness of AI-driven voice interactions.
Listen Before You Speak: Response Planning from Listener Facial Reactions for Conversational Speech Generation
arXiv:2609.21683v1 Announce Type: new Abstract: Conversational speech depends on dialogue context and the listener's immediately preceding behavior. We propose ReACT-TTS, a two-stage framework that uses a one-second pre-response listener facial sequence to plan the next utterance's emotion and prosody before speech realization. On a strict dyadic MELD protocol, Temporal conditioning yields higher mean macro-F1 and VAD concordance than Text-only across ten seeds, while accuracy remains essentially unchanged. Ablations show that temporal modeling performs best among the visual variants and that an explicit early-to-late difference is unnecessary; correct listener reactions also outperform cyclic mismatches on average. In a contextual-appropriateness study with 20 speech researchers, 76% of judgments prefer Temporal, 9% Text-only, and 15% report no preference. We further connect the predicted response style to a Grad-TTS backbone for end-to-end speech realization. Overall, the results support pre-response listener dynamics as complementary cues for conversational response planning. The source code is available at https://github.com/CYJ1/ReACT-TTS_public.
Abstract:Conversational speech depends on dialogue context and the listener's immediately preceding behavior. We propose ReACT-TTS, a two-stage framework that uses a one-second pre-response listener facial sequence to plan the next utterance's emotion and prosody before speech realization. On a strict dyadic MELD protocol, Temporal conditioning yields higher mean macro-F1 and VAD concordance than Text-only across ten seeds, while accuracy remains essentially unchanged. Ablations show that temporal modeling performs best among the visual variants and that an explicit early-to-late difference is unnecessary; correct listener reactions also outperform cyclic mismatches on average. In a contextual-appropriateness study with 20 speech researchers, 76% of judgments prefer Temporal, 9% Text-only, and 15% report no preference. We further connect the predicted response style to a Grad-TTS backbone for end-to-end speech realization. Overall, the results support pre-response listener dynamics as complementary cues for conversational response planning. The source code is available at this https URL.
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD)
arXiv:2609.21683 [cs.AI]
(or arXiv:2609.21683v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.21683 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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