activity
20232026
most citedThe Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts

3 citations · 5 across the 13 of their papers we have counts for

collaborators

13 papers

cs.SD2026

Is Prosody Lost in Translation? Fine-Grained Cross-Lingual Prosody Similarity Across Languages

Haopeng Xie, Ismail Rasim Ulgen, Sofia Son +2

Prosody plays an important role in speech translation, conveying information such as emphasis, emotion, and intent beyond lexical content. However, despite recent progress in expre…

cs.CL2026

Multilingual Emotion Neurons in Large Audio-Language Models

Xiutian Zhao, Philipp Koehn, Björn Schuller +1

Emotion is central to human communication, and its expression varies across languages. Large audio-language models (LALMs) achieve strong performance on multilingual speech tasks,…

eess.AS2026

TRACE-EVC: Text-Guided Relative Affective Control for Zero-Shot Emotional Voice Conversion

Zihan Zhang, Shreeram Suresh Chandra, Zongyang Du +5

Traditional emotional voice conversion (EVC) conditions generation on explicit target emotions like labels or references, defining the target affective state but omitting the direc…

eess.AS2026

DiffAnon: Diffusion-based Prosody Control for Voice Anonymization

Ismail Rasim Ulgen, Zexin Cai, Nicholas Andrews +2

To preserve or not to preserve prosody is a central question in voice anonymization. Prosody conveys meaning and affect, yet is tightly coupled with speaker identity. Existing meth…

cs.CL2026

Neuron-Level Emotion Control in Speech-Generative Large Audio-Language Models

Xiutian Zhao, Ismail Rasim Ulgen, Philipp Koehn +2

Large audio-language models (LALMs) can produce expressive speech, yet reliable emotion control remains elusive: conversions often miss the target affect and may degrade linguistic…

cs.CV2026

FlowPortrait: Reinforcement Learning for Audio-Driven Portrait Video Generation

Weiting Tan, Andy T. Liu, Ming Tu +3

Generating realistic talking-head videos remains challenging due to persistent issues such as imperfect lip synchronization, unnatural motion, and evaluation metrics that correlate…

cs.CL2025

Process-Supervised Reinforcement Learning for Interactive Multimodal Tool-Use Agents

Weiting Tan, Xinghua Qu, Ming Tu +4

Effective interactive tool use requires agents to master Tool Integrated Reasoning (TIR): a complex process involving multi-turn planning and long-context dialogue management. To t…

cs.CL2024

X-ALMA: Plug & Play Modules and Adaptive Rejection for Quality Translation at Scale

Haoran Xu, Kenton Murray, Philipp Koehn +3

Large language models (LLMs) have achieved remarkable success across various NLP tasks with a focus on English due to English-centric pre-training and limited multilingual data. In…

cs.CL2024

DiffNorm: Self-Supervised Normalization for Non-autoregressive Speech-to-speech Translation

Weiting Tan, Jingyu Zhang, Lingfeng Shen +2

Non-autoregressive Transformers (NATs) are recently applied in direct speech-to-speech translation systems, which convert speech across different languages without intermediate tex…

cs.CL2024★ 2 cited

Streaming Sequence Transduction through Dynamic Compression

Weiting Tan, Yunmo Chen, Tongfei Chen +5

We introduce STAR (Stream Transduction with Anchor Representations), a novel Transformer-based model designed for efficient sequence-to-sequence transduction over streams. STAR dyn…

cs.CL2024★ 3 cited

The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts

Lingfeng Shen, Weiting Tan, Sihao Chen +6

As the influence of large language models (LLMs) spans across global communities, their safety challenges in multilingual settings become paramount for alignment research. This pap…

cs.CL2023

Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles

Weiting Tan, Haoran Xu, Lingfeng Shen +5

Large language models trained primarily in a monolingual setting have demonstrated their ability to generalize to machine translation using zero- and few-shot examples with in-cont…

cs.CL2023

Error Norm Truncation: Robust Training in the Presence of Data Noise for Text Generation Models

Tianjian Li, Haoran Xu, Philipp Koehn +2

Text generation models are notoriously vulnerable to errors in the training data. With the wide-spread availability of massive amounts of web-crawled data becoming more commonplace…