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StellarTTS: Sparse Temporal Embedding for Low-Latency and Robust Speech Synthesis

2026-07-22 07:48:37
Kaicheng Luo, Xuefei Gong, Yutao Sun, Jinling He, Yujie Hou, Xiaoyang Xing, Huiyan Li, Bing Han, Yanmin Qian
arXiv_SD
           
Abstract

The trade-off between robustness, latency, and prosody critically challenges text-to-speech (TTS) systems. Autoregressive models, despite fidelity, are slow and error-prone; non-autoregressive (NAR) alternatives, while fast, often sacrifice prosodic naturalness via rigid alignments. This paper introduces StellarTTS, a novel mobile-optimized NAR TTS framework based on a sparse temporal embedding strategy, enabling granular control of phoneme duration, pronunciation, and prosody. Furthermore, we propose a semantic-aware codec that facilitates efficient single-stage decoding. Conditioned on the sparse temporal embedding, our 83M-parameter lightweight masked generative transformer achieves a real-time factor (RTF) of 0.08. Experiments demonstrate that StellarTTS attains lower latency and stronger robustness compared to state-of-the-art TTS systems, while maintaining competitive performance in audio quality, prosodic naturalness, and speaker similarity.

Abstract (translated)

URL

https://arxiv.org/abs/2607.19859

PDF

https://arxiv.org/pdf/2607.19859.pdf

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