collaborators

13 papers

eess.AS2026

MSVAE: Rethinking Musical Semantics in Continuous Latent Spaces

Guobin Ma, Yuepeng Jiang, Dake Guo +15

Continuous latent representations underlie recent diffusion-based lyrics-to-song and text-to-music systems, yet aligning lyric conditions with these latents remains difficult: exis…

eess.AS2026

StreamTN: A Low-Latency Streaming Chinese Text Normalization Model for Streaming TTS in Dialogue Systems

Wenhao Li, Jinrui Liang, Haoyu Zhang +11

Text-to-Speech (TTS) is an essential module that provides spoken responses in a spoken dialogue system (SDS) centered on a large language model (LLM). To ensure accurate TTS synthe…

eess.AS2026

Rethinking Music Tokenization: A Semantic Codec toward High-Fidelity LLM Music Generation

Huakang Chen, Guobin Ma, Yuepeng Jiang +10

Discrete audio tokenization has become the critical interface between raw waveforms and autoregressive modeling in recent music generation. As a result, music tokenizers must simul…

eess.AS2026

SphereVAE: Hyperspherical Latent Autoencoders for Robust Autoregressive Speech Representation Modeling

Haoyu Zhang, Jingbin Hu, Hanke Xie +8

With the rapid development of speech generation technology, discrete codec representations have been widely used because they provide a stable prediction paradigm. In expressive sp…

eess.AS2026

SemBridge: Semantic Token Anchoring for Continuous-Latent Autoregressive Speech Generation

Hanke Xie, Haopeng Lin, Jiale Qian +13

Continuous-latent autoregressive speech generation has emerged as a promising alternative to discrete-token modeling by avoiding quantization loss and preserving richer acoustic in…

eess.AS2026

MeanVC 2: Robust Low-Latency Streaming Zero-Shot Voice Conversion

Guobin Ma, Yuxuan Xia, Yuepeng Jiang +6

Streaming zero-shot voice conversion (VC) has become increasingly popular due to its potential for real-time applications. The recently proposed MeanVC achieves lightweight streami…

eess.AS2026

G-MaP-SE: Guided Speech Enhancement via GMM-Based Prior Matching

Yike Zhu, Ziqian Wang, Zikai Liu +5

Using speaker embeddings as conditioning can strengthen speech enhancement, but most methods either require clean enrollment audio or rely on embeddings extracted from noisy speech…

cs.SD2026

Joint Learning Global-Local Speaker Classification to Enhance End-to-End Speaker Diarization and Recognition

Yuhang Dai, Haopeng Lin, Jiale Qian +9

Large Audio-Language Models (LALMs) have demonstrated remarkable performance in end-to-end speaker diarization and recognition. However, their speaker discriminability remains limi…

eess.AS2026

OmniCodec: Low Frame Rate Universal Audio Codec with Semantic-Acoustic Disentanglement

Jingbin Hu, Haoyu Zhang, Dake Guo +10

Large Language Models (LLMs) have advanced audio generation through discrete representation learning. However, most existing neural codecs focus on speech and emphasize reconstruct…

eess.AS2025

SoulX-Podcast: Towards Realistic Long-form Podcasts with Dialectal and Paralinguistic Diversity

Hanke Xie, Haopeng Lin, Wenxiao Cao +17

Recent advances in text-to-speech (TTS) synthesis have significantly improved speech expressiveness and naturalness. However, most existing systems are tailored for single-speaker…

eess.AS2025

ContextASR-Bench: A Massive Contextual Speech Recognition Benchmark

He Wang, Linhan Ma, Dake Guo +4

Automatic Speech Recognition (ASR) has been extensively investigated, yet prior benchmarks have largely focused on assessing the acoustic robustness of ASR models, leaving evaluati…

cs.SD2025

StreamFlow: Streaming Flow Matching with Block-wise Guided Attention Mask for Speech Token Decoding

Dake Guo, Jixun Yao, Linhan Ma +2

Recent advancements in discrete token-based speech generation have highlighted the importance of token-to-waveform generation for audio quality, particularly in real-time interacti…

eess.AS2025

FlexSpeech: Towards Stable, Controllable and Expressive Text-to-Speech

Linhan Ma, Dake Guo, He Wang +2

Current speech generation research can be categorized into two primary classes: non-autoregressive and autoregressive. The fundamental distinction between these approaches lies in…