# Prosodic Boundary-Aware Streaming Generation for LLM-Based TTS with Streaming Text Input

arXiv: https://arxiv.org/abs/2603.06444 · HTML: https://mlsanity.com/paper/2603.06444/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 26 references that are in the map.
- First posted on arXiv 6 Mar 2026.

## Builds on (most cited first, 22 of 22)

- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers](https://mlsanity.com/paper/2301.02111/) (2023)
- [AudioLM: a Language Modeling Approach to Audio Generation](https://mlsanity.com/paper/2209.03143/) (2022)
- [Moshi: a speech-text foundation model for real-time dialogue](https://mlsanity.com/paper/2410.00037/) (2024)
- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [Seed-TTS: A Family of High-Quality Versatile Speech Generation Models](https://mlsanity.com/paper/2406.02430/) (2024)
- [CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models](https://mlsanity.com/paper/2412.10117/) (2024)
- [emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation](https://mlsanity.com/paper/2312.15185/) (2023)
- [CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-training](https://mlsanity.com/paper/2505.17589/) (2025)
- [Spirit LM: Interleaved Spoken and Written Language Model](https://mlsanity.com/paper/2402.05755/) (2024)
- [Qwen3-TTS Technical Report](https://mlsanity.com/paper/2601.15621/) (2026)
- [A3T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing](https://mlsanity.com/paper/2203.09690/) (2022)
- [LiveSpeech: Low-Latency Zero-shot Text-to-Speech via Autoregressive Modeling of Audio Discrete Codes](https://mlsanity.com/paper/2406.02897/) (2024)
- [Speak While You Think: Streaming Speech Synthesis During Text Generation](https://mlsanity.com/paper/2309.11210/) (2023)
- [SpeakStream: Streaming Text-to-Speech with Interleaved Data](https://mlsanity.com/paper/2505.19206/) (2025)
- [Transduce and Speak: Neural Transducer for Text-to-Speech with Semantic Token Prediction](https://mlsanity.com/paper/2311.02898/) (2023)
- [Zero-Shot Text-to-Speech from Continuous Text Streams](https://mlsanity.com/paper/2410.00767/) (2024)
- [Word-Level Emotional Expression Control in Zero-Shot Text-to-Speech Synthesis](https://mlsanity.com/paper/2509.24629/) (2025)
- [Interleaved Speech-Text Language Models for Simple Streaming Text-to-Speech Synthesis](https://mlsanity.com/paper/2412.16102/) (2024)
- [Zero-Shot Streaming Text to Speech Synthesis with Transducer and Auto-Regressive Modeling](https://mlsanity.com/paper/2505.19669/) (2025)
- [StreamMel: Real-Time Zero-shot Text-to-Speech via Interleaved Continuous Autoregressive Modeling](https://mlsanity.com/paper/2506.12570/) (2025)

## Built on by (most cited first, 1 of 1)

- [X2Streaming-TTS: Causal Token-Level Text-to-Speech from Streaming Text with Speech-State Inheritance](https://mlsanity.com/paper/2608.18661/) (2026)

## Abstract (arXiv)

Streaming TTS that receives streaming text is essential for interactive systems, yet this scheme faces two major challenges: unnatural prosody due to missing lookahead and long-form collapse due to unbounded context. We propose a prosodic-boundary-aware post-training strategy, adapting a pretrained LLM-based TTS model using weakly time-aligned data. Specifically, the model is adapted to learn early stopping at specified content boundaries when provided with limited future text. During inference, a sliding-window prompt carries forward previous text and speech tokens, ensuring bounded context and seamless concatenation. Evaluations show our method outperforms CosyVoice-Style interleaved baseline in both short and long-form scenarios. In long-text synthesis, especially, it achieves a 66.2% absolute reduction in word error rate (from 71.0% to 4.8%) and increases speaker and emotion similarity by 16.1% and 1.5% relatively, offering a robust solution for streaming TTS with incremental text.