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cs.SD2026

Qwen-Audio-3.1-ASR Technical Report

Chuanmeng Bian, Daren Chen, Peixin Chen +45

We present Qwen-Audio-3.1-ASR, a family of audio-language models that builds upon its predecesor Qwen-Audio-3.0-ASR with improved speech recognition and expanded capabilities in sp…

cs.SD2026

Listen, Do Not Copy: Internalizing Audio-Grounded Scaffold Context for Robust Omni-Model Speech Understanding

Pengfei Zhang, Biao Tian, Tianxin Xie +3

Omni models transcribe clean, single-speaker speech well, but their accuracy drops sharply when speakers overlap and the scene is noisy, exactly where knowing who said what matters…

cs.SD2026

Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering

Junyu Dai, Xinyue Fan, Weiqin Li +14

In this report, we present a unified song generation framework capable of producing high-quality full-length music from lyrics, text descriptions, and musical attributes. The propo…

cs.SD2026

TF-MossFormer: Integrating Convolution Gated Local-Global Attentions for Enhanced Time-Frequency Domain Monaural Speech Separation

Shengkui Zhao, Zexu Pan, Haoxu Wang +3

Transformers with global attention capture long-range dependencies but can miss the fine-grained local continuity crucial for speech separation. We propose TF-MossFormer, a time-fr…

cs.SD2026

E2E-AEC: Implementing an end-to-end neural network learning approach for acoustic echo cancellation

Yiheng Jiang, Biao Tian, Haoxu Wang +4

We propose a novel neural network-based end-to-end acoustic echo cancellation (E2E-AEC) method capable of streaming inference, which operates effectively without reliance on tradit…

cs.SD2025

Exploring Efficient Directional and Distance Cues for Regional Speech Separation

Yiheng Jiang, Haoxu Wang, Yafeng Chen +2

In this paper, we introduce a neural network-based method for regional speech separation using a microphone array. This approach leverages novel spatial cues to extract the sound s…

cs.SD2025

A Small-footprint Acoustic Echo Cancellation Solution for Mobile Full-Duplex Speech Interactions

Yiheng Jiang, Tian Biao

In full-duplex speech interaction systems, effective Acoustic Echo Cancellation (AEC) is crucial for recovering echo-contaminated speech. This paper presents a neural network-based…

cs.SD2025

ZipEnhancer: Dual-Path Down-Up Sampling-based Zipformer for Monaural Speech Enhancement

Haoxu Wang, Biao Tian

In contrast to other sequence tasks modeling hidden layer features with three axes, Dual-Path time and time-frequency domain speech enhancement models are effective and have low pa…