X2-NativeCursor is proposed, a lightweight observer that tracks progress from native speech tokens before waveform decoding without changing the TTS generator, and its normalization plan links spoken labels to their original-text spans.
Abstract
Incremental-text streaming text-to-speech (TTS) needs online text progress tracking for synchronized highlighting, interruption handling, and dialogue-history updates. Input text arrives before it is spoken, so text arrival alone cannot indicate speech progress. Existing waveform-based alignment requires complete audio or adds acoustic processing during streaming. We propose X2-NativeCursor, a lightweight observer that tracks progress from native speech tokens before waveform decoding without changing the TTS generator. Its normalization plan links spoken labels to their original-text spans. Text and native-token encoders feed a local matcher that estimates the current label position. A separate output rule converts revisable position estimates into a cursor that never moves backward. Mean absolute error against an automatic reference is 0.151 Chinese characters with 80-ms lookahead, versus 1.253 characters with 320-ms lookahead for an online waveform baseline. Alignment real-time factor also decreases from 0.3598 to 0.0180 relative to this baseline. Lower tracking error is retained under a second automatic alignment reference. We evaluate X2-NativeCursor on Qwen3-TTS and validate its adaptation to CosyVoice2 by training a separate observer for each backbone. Code is publicly available at https://github.com/X-Square-Robot/X2Streaming-TTS.
X2Streaming-TTS is presented, a causal TTS framework that consumes asynchronously arriving text tokens and emits speech without accessing future input that outperforms existing pseudo-streaming models on most subjective and objective metrics.
StreamTN is proposed, a lightweight LLM-based Chinese streaming TN model that employs a dual-track streaming framework in which input tokens and output tokens are processed on two parallel tracks, enabling low-latency real-time inference without complex prompting.
Wen-Hao Li, Jin-Rui Liang, Hao-Yu Zhang et al.· 0 citations
StreamAlign performs online speech-text alignment by combining character-level RNN-Transducer alignment with word-level ASR guidance, mitigating ASR-LLM vocabulary mismatch while preserving recognition accuracy, and achieves the lowest WER and highest UTMOS among evaluated tokenizers.
Kang-wook Kim, Jinyoung Park, Jinsoo Kim et al.· 0 citations
Alignment-Free Text-Audiobox (Text-AB), a unified framework for high-quality voice dubbing and full-duplex dialogue synthesis, which delivers a step-change improvement over the latest internal dubbing system with large gains in prosody similarity, voice similarity, naturalness, and shareability.
Sanyuan Chen, Min-Jae Hwang, Sho Inoue et al.· 0 citations
FuseAlign, a transformer-based aligner trained on large-scale pseudo-labeled speech with online label correction, is introduced, a transformer-based aligner trained on large-scale pseudo-labeled speech with online label correction that substantially outperforms all baselines and remains robust under real ASR transcript...
Mithilesh Vaidya, Stephen W. Bailey, Sumukh Badam et al.· 0 citations
X2Streaming-ASR is proposed, which decomposes streaming recognition into when to commit and what to commit, and achieves the best streaming CER among the evaluated systems on AISHELL-1 and AISHELL-3 with substantially lower latency.
Zhi-Wei Lin, Kaiqi Fu, Rime Wen et al.· 0 citations
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