CTC-TTS: LLM-based dual-streaming text-to-speech with CTC alignment
Abstract
LLM-based text-to-speech (TTS) systems can generate natural speech, but most are not designed for low-latency dual-streaming synthesis. Dual-streaming TTS requires accurate text--speech alignment and training sequences that balance synthesis quality and latency. Prior work often relies on GMM--HMM forced alignment (e.g., MFA), which is pipeline-heavy; fixed-ratio interleaving provides weak alignment regularity. We propose CTC-TTS, which replaces MFA with CTC phoneme--speech alignment and introduces a bi-word text--speech interleaving strategy. Two variants are designed: CTC-TTS-L (length-wise token concatenation) for higher quality and CTC-TTS-F (feature-wise embedding stacking) for lower latency. Experiments show that CTC-TTS outperforms strong baselines on streaming synthesis and zero-shot tasks.
Single-speaker Streaming Experiments
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