Jinling He | Searcharxiv

arXiv2026

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

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.

cs.SD↗

arXiv2024

Diff-DTI: Fast Diffusion Tensor Imaging Using A Feature-Enhanced Joint Diffusion Model

Magnetic resonance diffusion tensor imaging (DTI) is a critical tool for neural disease diagnosis. However, long scan time greatly hinders the widespread clinical use of DTI. To accelerate image acquisition, a feature-enhanced joint diffusion model (Diff-DTI) is proposed to obtain accurate DTI parameter maps from a limited number of diffusion-weighted images (DWIs). Diff-DTI introduces a joint diffusion model that directly learns the joint probability distribution of DWIs with DTI parametric maps for conditional generation. Additionally, a feature enhancement fusion mechanism (FEFM) is designed and incorporated into the generative process of Diff-DTI to preserve fine structures in the generated DTI maps. A comprehensive evaluation of the performance of Diff-DTI was conducted on the Human Connectome Project dataset. The results demonstrate that Diff-DTI outperforms existing state-of-the-art fast DTI imaging methods in terms of visual quality and quantitative metrics. Furthermore, Diff-DTI has shown the ability to produce high-fidelity DTI maps with only three DWIs, thus overcoming the requirement of a minimum of six DWIs for DTI.

eess.IV↗