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Research questionHow can speech synthesis produce natural dubbing and full-duplex dialogue without forced alignment or explicit duration prediction?Dubbing and full-duplex conversation require timing to emerge alongside voice, prosody, and interaction rather than from precomputed alignments. Without explicit durations, the generator must still coordinate text with natural turn-taking and sustained audio output.
AI
Audio & Speech
Audio & Speech Processing
Diffusion Models
Machine Learning
Latest papersRecent research connected to this question, newest first.Alignment-Free Text-Audiobox for Voice Dubbing and Full-Duplex Dialogue SynthesisThe source concerns a 3B-parameter latent diffusion speech model that learns text–speech alignment through cross-attention from raw text, using DAC-VAE audio latents. It covers cross-lingual dubbing, one-shot generation of roughly one minute, arbitrarily long generation, and full-duplex dialogue with turn-taking, back-channeling, and emotional dynamics. Reported evidence includes a real-world dubbing benchmark and comparisons with internal systems; broader deployment performance is not established.research paper · Sep 3, 2026
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How can full-duplex dialogue models learn natural acoustic turn-taking without degrading semantic responses?How can controlled synthetic dialogues satisfy intended emotion and intent while remaining natural to human readers?How can creators generate reusable multi-speaker voices and expressive audio scenes from instructions or reference recordings?How can full-duplex voice agents infer role-implied behavior while managing overlapping speech and conflicting instructions in real time?