1 citations · 1 across the 16 of their papers we have counts for
17 papers
MMAC: A Massive Multi-dimensional Benchmark for Audio Captioning
Weijie Wu, Junbo Li, Lin Li +2
With the development of audio large language models (AudioLLMs), audio captioning needs to move from brief descriptions toward open-ended and fine-grained free-form descriptions. E…
HoliDubber: Holistic Video Dubbing for Complex Acoustic Scenes via Text-Guided Audio Synthesis
Wenhao Guan, Yifan Duan, Junxi Liu +6
Video dubbing is a cornerstone of multimedia content creation, aiming to synthesize synchronized acoustic sequences for visual streams. While Text-to-Speech (TTS) and Text-to-Audio…
SARA: A Dual-Stream VAE for High-Fidelity Speech Generation via Integrating Semantic and Acoustic Representations
Peijie Chen, Wenhao Guan, Weijie Wu +7
Zero-shot text-to-speech (TTS) relies on robust speech representations. However, current speech tokenizers face a fundamental trade-off: acoustic codecs preserve high-fidelity audi…
AlphaFlowTSE: One-Step Generative Target Speaker Extraction via Conditional AlphaFlow
Duojia Li, Shuhan Zhang, Zihan Qian +5
In target speaker extraction (TSE), we aim to recover target speech from a multi-talker mixture using a short enrollment utterance as reference. Recent studies on diffusion and flo…
SyncVoice: Simple and Effective Automatic Video Dubbing with Vision-Augmented TTS
Kaidi Wang, Yi He, Wenhao Guan +9
Automatic video dubbing aims to generate high-fidelity speech that is temporally aligned with visual content. However, existing methods still suffer from limited speech naturalness…
UniVoice: Unifying Autoregressive ASR and Flow-Matching based TTS with Large Language Models
Wenhao Guan, Zhikang Niu, Ziyue Jiang +5
Large language models (LLMs) have demonstrated promising performance in both automatic speech recognition (ASR) and text-to-speech (TTS) systems, gradually becoming the mainstream…
XMUspeech Systems for the ASVspoof 5 Challenge
Wangjie Li, Xingjia Xie, Yishuang Li +5
In this paper, we present our submitted XMUspeech systems to the speech deepfake detection track of the ASVspoof 5 Challenge. Compared to previous challenges, the audio duration in…
Phoenix-VAD: Streaming Semantic Endpoint Detection for Full-Duplex Speech Interaction
Weijie Wu, Wenhao Guan, Kaidi Wang +6
Spoken dialogue models have significantly advanced intelligent human-computer interaction, yet they lack a plug-and-play full-duplex prediction module for semantic endpoint detecti…
MeanFlowSE: one-step generative speech enhancement via conditional mean flow
Duojia Li, Shenghui Lu, Hongchen Pan +3
Multistep inference is a bottleneck for real-time generative speech enhancement because flow- and diffusion-based systems learn an instantaneous velocity field and therefore rely o…
ReFlow-VC: Zero-shot Voice Conversion Based on Rectified Flow and Speaker Feature Optimization
Pengyu Ren, Wenhao Guan, Kaidi Wang +3
In recent years, diffusion-based generative models have demonstrated remarkable performance in speech conversion, including Denoising Diffusion Probabilistic Models (DDPM) and othe…
A Two-Stage Hierarchical Deep Filtering Framework for Real-Time Speech Enhancement
Shenghui Lu, Hukai Huang, Jinanglong Yao +3
This paper proposes a model that integrates sub-band processing and deep filtering to fully exploit information from the target time-frequency (TF) bin and its surrounding TF bins…
DS-Codec: Dual-Stage Training with Mirror-to-NonMirror Architecture Switching for Speech Codec
Peijie Chen, Wenhao Guan, Kaidi Wang +4
Neural speech codecs are essential for advancing text-to-speech (TTS) systems. With the recent success of large language models in text generation, developing high-quality speech t…
Discl-VC: Disentangled Discrete Tokens and In-Context Learning for Controllable Zero-Shot Voice Conversion
Kaidi Wang, Wenhao Guan, Ziyue Jiang +5
Currently, zero-shot voice conversion systems are capable of synthesizing the voice of unseen speakers. However, most existing approaches struggle to accurately replicate the speak…
SlimSpeech: Lightweight and Efficient Text-to-Speech with Slim Rectified Flow
Kaidi Wang, Wenhao Guan, Shenghui Lu +3
Recently, flow matching based speech synthesis has significantly enhanced the quality of synthesized speech while reducing the number of inference steps. In this paper, we introduc…
Enhancing Code-Switching Speech Recognition with LID-Based Collaborative Mixture of Experts Model
Hukai Huang, Jiayan Lin, Kaidi Wang +4
Due to the inherent difficulty in modeling phonetic similarities across different languages, code-switching speech recognition presents a formidable challenge. This study proposes…
Dynamic Language Group-Based MoE: Enhancing Code-Switching Speech Recognition with Hierarchical Routing
Hukai Huang, Shenghui Lu, Yahui Shan +5
The Mixture of Experts (MoE) model is a promising approach for handling code-switching speech recognition (CS-ASR) tasks. However, the existing CS-ASR work on MoE has yet to levera…
LAFMA: A Latent Flow Matching Model for Text-to-Audio Generation
Wenhao Guan, Kaidi Wang, Wangjin Zhou +6
Recently, the application of diffusion models has facilitated the significant development of speech and audio generation. Nevertheless, the quality of samples generated by diffusio…