collaborators

9 papers

eess.AS2026

The SLT 2026 SmartGlasses Challenge: Benchmarking Egocentric Multi-Talker Speech Recognition and Understanding with Audio-Language Models

Dehui Gao, Zhixian Zhao, Zhennan Lin +14

Recent advances in large language models (LLMs) and multimodal LLMs (MLLMs) have created new opportunities for wearable speech interfaces, with smart glasses providing an egocentri…

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

Towards Fine-Grained Multi-Dimensional Speech Understanding: Data Pipeline, Benchmark, and Model

Guojian Li, Zhixian Zhao, Zhennan Lin +9

While speech Large Language Models (LLMs) excel at conventional tasks like basic speech recognition, they lack fine-grained, multi-dimensional perception. This deficiency is eviden…

eess.AS2026

DiffRhythm 2: Efficient and High Fidelity Song Generation via Block Flow Matching

Yuepeng Jiang, Huakang Chen, Ziqian Ning +7

Generating full-length, high-quality songs is challenging, as it requires maintaining long-term coherence both across text and music modalities and within the music modality itself…

cs.SD2025

Serial-Parallel Dual-Path Architecture for Speaking Style Recognition

Guojian Li, Qijie Shao, Zhixian Zhao +3

Speaking Style Recognition (SSR) identifies a speaker's speaking style characteristics from speech. Existing style recognition approaches primarily rely on linguistic information,…

cs.CL2025

Easy Turn: Integrating Acoustic and Linguistic Modalities for Robust Turn-Taking in Full-Duplex Spoken Dialogue Systems

Guojian Li, Chengyou Wang, Hongfei Xue +8

Full-duplex interaction is crucial for natural human-machine communication, yet remains challenging as it requires robust turn-taking detection to decide when the system should spe…