15 papers
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…
The Platonic brain bridge hypothesis: human brain networks as an architectural prior for multimodal large language models
Pengfei Zhang, Biao Tian, Xiangang Li +1
Multimodal large language models predict brain activity, but brain alignment has been a measurement, not a design tool. We propose the Platonic brain bridge hypothesis: omni models…
Qwen-Audio-3.0-ASR Technical Report
Chuanmeng Bian, Daren Chen, Peixin Chen +42
In recent years, automatic speech recognition (ASR) has witnessed transformative advancements driven by three complementary paradigms: data scaling, model scaling, and deep integra…
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…
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…
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…
FlowTTS-GRPO: Online Reinforcement Learning with Multi-Objective Reward Optimization for Flow-Matching Based Text-to-Speech
Haoxu Wang, Biao Tian, Weiqin Li +3
Existing Reinforcement Learning (RL) research for Text-to-Speech (TTS) focuses on large language models (LLMs), leaving Flow-Matching (FM) under-explored. We present FlowTTS-GRPO,…
LuSeeL: Language-queried Binaural Universal Sound Event Extraction and Localization
Zexu Pan, Shengkui Zhao, Yukun Ma +4
Most universal sound extraction algorithms focus on isolating a target sound event from single-channel audio mixtures. However, the real world is three-dimensional, and binaural au…
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…
FlowSE-GRPO: Training Flow Matching Speech Enhancement via Online Reinforcement Learning
Haoxu Wang, Biao Tian, Yiheng Jiang +5
Generative speech enhancement offers a promising alternative to traditional discriminative methods by modeling the distribution of clean speech conditioned on noisy inputs. Post-tr…
Fun-ASR Technical Report
Keyu An, Yanni Chen, Zhigao Chen +35
In recent years, automatic speech recognition (ASR) has witnessed transformative advancements driven by three complementary paradigms: data scaling, model size scaling, and deep in…
FLASepformer: Efficient Speech Separation with Gated Focused Linear Attention Transformer
Haoxu Wang, Yiheng Jiang, Gang Qiao +2
Speech separation always faces the challenge of handling prolonged time sequences. Past methods try to reduce sequence lengths and use the Transformer to capture global information…
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…
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…
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…