CTC-TTS: LLM-based dual-streaming text-to-speech with CTC alignment

Abstract:Large-language-model (LLM)-based text-to-speech (TTS) systems can generate natural speech, but most are not designed for low-latency dual-streaming synthesis. High-quality dual-streaming TTS depends on accurate text--speech alignment and well-designed training sequences that balance synthesis quality and latency. Prior work often relies on GMM-HMM based forced-alignment toolkits (e.g., MFA), which are pipeline-heavy and less flexible than neural aligners; fixed-ratio interleaving of text and speech tokens struggles to capture text--speech alignment regularities. We propose CTC-TTS, which replaces MFA with a CTC based aligner and introduces a bi-word based interleaving strategy. Two variants are designed: CTC-TTS-L (token concatenation along the sequence length) for higher quality and CTC-TTS-F (embedding stacking along the feature dimension) for lower latency. Experiments show that CTC-TTS outperforms fixed-ratio interleaving and MFA-based baselines on streaming synthesis and zero-shot tasks.

Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Sound (cs.SD)

arXiv:2602.19574 [eess.AS]

(or arXiv:2602.19574v2 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2602.19574 Focus to learn more arXiv-issued DOI via DataCite

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