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20242026
most citedLLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2026

Evaluating and Preserving Lexical Stress in English-to-Chinese Speech-to-Speech Translation

Yuchen Song, Xi Chen, Mingze Li +1

Speech-to-speech translation (S2ST) systems have achieved impressive progress in semantic accuracy and speech naturalness. However, the cross-lingual transfer of lexical stress, a…

eess.AS2026

Beyond Acoustic Sparsity and Linguistic Bias: A Prompt-Free Paradigm for Mispronunciation Detection and Diagnosis

Haopeng Geng, Longfei Yang, Xi Chen +3

Mispronunciation Detection and Diagnosis (MDD) requires modeling fine-grained acoustic deviations. However, current ASR-derived MDD systems often face inherent limitations. In part…

cs.CL2025

StressTransfer: Stress-Aware Speech-to-Speech Translation with Emphasis Preservation

Xi Chen, Yuchen Song, Satoshi Nakamura

We propose a stress-aware speech-to-speech translation (S2ST) system that preserves word-level emphasis by leveraging LLMs for cross-lingual emphasis conversion. Our method transla…

cs.CL2025

SASST: Leveraging Syntax-Aware Chunking and LLMs for Simultaneous Speech Translation

Zeyu Yang, Lai Wei, Roman Koshkin +2

This work proposes a grammar-based chunking strategy that segments input streams into semantically complete units by parsing dependency relations (e.g., noun phrase boundaries, ver…

cs.CL2024★ 1 cited

LLaST: Improved End-to-end Speech Translation System Leveraged by Large Language Models

Xi Chen, Songyang Zhang, Qibing Bai +2

We introduces LLaST, a framework for building high-performance Large Language model based Speech-to-text Translation systems. We address the limitations of end-to-end speech transl…