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cs.SD2026

Luna-TTS Family Technical Report

Feng Yin, Shuai Shi, Junjie Zheng +19

Modern text-to-speech (TTS) is dominated by autoregressive (AR) codec language models, whose left-to-right decoding brings latency that grows with utterance length, error accumulat…

cs.SD2026

On the Distillation Loss Functions of Speech VAE for Unified Reconstruction, Understanding, and Generation

Changhao Cheng, Wei Wang, Wangyou Zhang +4

Continuous speech representations based on Variational Autoencoders (VAEs) have emerged as a promising alternative to traditional spectrogram or discrete token based features for s…

cs.DC2026

LegoDiffusion: Micro-Serving Text-to-Image Diffusion Workflows

Lingyun Yang, Suyi Li, Tianyu Feng +10

Text-to-image generation executes a diffusion workflow comprising multiple models centered on a base diffusion model. Existing serving systems treat each workflow as an opaque mono…

cs.CV2026

Exploring Motion-Language Alignment for Text-driven Motion Generation

Ruxi Gu, Zilei Wang, Wei Wang

Text-driven human motion generation aims to synthesize realistic motion sequences that follow textual descriptions. Despite recent advances, accurately aligning motion dynamics wit…

cs.SD2026

DSFlow: Dual Supervision and Step-Aware Architecture for One-Step Flow Matching Speech Synthesis

Bin Lin, Peng Yang, Chao Yan +5

Flow-matching models have enabled high-quality text-to-speech synthesis, but their iterative sampling process during inference incurs substantial computational cost. Although disti…

eess.AS2026

Representation-Regularized Convolutional Audio Transformer for Audio Understanding

Bing Han, Chushu Zhou, Yifan Yang +4

Bootstrap-based Self-Supervised Learning (SSL) has achieved remarkable progress in audio understanding. However, existing methods typically operate at a single level of granularity…