2 citations · 5 across the 16 of their papers we have counts for
16 papers
SPRI: SVD-Partitioned Residual Initialization for Data-Constrained MoE Upcycling
Weiqiao Shan, Ruixiang Mao, Yuang Li +10
Mixture-of-Experts (MoE) models enable efficient scaling, but training them from scratch remains prohibitively expensive. MoE upcycling mitigates this cost by converting pretrained…
DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation
Xinglin Lyu, Wei Tang, Yuang Li +7
Document-level context is crucial for handling discourse challenges in text-to-text document-level machine translation (MT). Despite the increased discourse challenges introduced b…
Enhancing Speech Large Language Models with Prompt-Aware Mixture of Audio Encoders
Weiqiao Shan, Yuang Li, Yuhao Zhang +9
Connecting audio encoders with large language models (LLMs) allows the LLM to perform various audio understanding tasks, such as automatic speech recognition (ASR) and audio captio…
Optimizing Speech Multi-View Feature Fusion through Conditional Computation
Weiqiao Shan, Yuhao Zhang, Yuchen Han +7
Recent advancements have highlighted the efficacy of self-supervised learning (SSL) features in various speech-related tasks, providing lightweight and versatile multi-view speech…
Investigating Numerical Translation with Large Language Models
Wei Tang, Jiawei Yu, Yuang Li +5
The inaccurate translation of numbers can lead to significant security issues, ranging from financial setbacks to medical inaccuracies. While large language models (LLMs) have made…
"I've Heard of You!": Generate Spoken Named Entity Recognition Data for Unseen Entities
Jiawei Yu, Xiang Geng, Yuang Li +8
Spoken named entity recognition (NER) aims to identify named entities from speech, playing an important role in speech processing. New named entities appear every day, however, ann…
Hard-Synth: Synthesizing Diverse Hard Samples for ASR using Zero-Shot TTS and LLM
Jiawei Yu, Yuang Li, Xiaosong Qiao +6
Text-to-speech (TTS) models have been widely adopted to enhance automatic speech recognition (ASR) systems using text-only corpora, thereby reducing the cost of labeling real speec…
Large Language Model Should Understand Pinyin for Chinese ASR Error Correction
Yuang Li, Xiaosong Qiao, Xiaofeng Zhao +4
Large language models can enhance automatic speech recognition systems through generative error correction. In this paper, we propose Pinyin-enhanced GEC, which leverages Pinyi, th…
Why Not Transform Chat Large Language Models to Non-English?
Xiang Geng, Ming Zhu, Jiahuan Li +14
The scarcity of non-English data limits the development of non-English large language models (LLMs). Transforming English-centric LLMs to non-English has been identified as an effe…
Cross-Domain Audio Deepfake Detection: Dataset and Analysis
Yuang Li, Min Zhang, Mengxin Ren +3
Audio deepfake detection (ADD) is essential for preventing the misuse of synthetic voices that may infringe on personal rights and privacy. Recent zero-shot text-to-speech (TTS) mo…
Using Large Language Model for End-to-End Chinese ASR and NER
Yuang Li, Jiawei Yu, Min Zhang +6
Mapping speech tokens to the same feature space as text tokens has become the paradigm for the integration of speech modality into decoder-only large language models (LLMs). An alt…
A Multitask Training Approach to Enhance Whisper with Contextual Biasing and Open-Vocabulary Keyword Spotting
Yuang Li, Min Zhang, Chang Su +7
The recognition of rare named entities, such as personal names and terminologies, is challenging for automatic speech recognition (ASR) systems, especially when they are not freque…
Accelerating Transducers through Adjacent Token Merging
Yuang Li, Yu Wu, Jinyu Li +1
Recent end-to-end automatic speech recognition (ASR) systems often utilize a Transformer-based acoustic encoder that generates embedding at a high frame rate. However, this design…
Prompting Large Language Models for Zero-Shot Domain Adaptation in Speech Recognition
Yuang Li, Yu Wu, Jinyu Li +1
The integration of Language Models (LMs) has proven to be an effective way to address domain shifts in speech recognition. However, these approaches usually require a significant a…
Accurate and Structured Pruning for Efficient Automatic Speech Recognition
Huiqiang Jiang, Li Lyna Zhang, Yuang Li +7
Automatic Speech Recognition (ASR) has seen remarkable advancements with deep neural networks, such as Transformer and Conformer. However, these models typically have large model s…
Self-Supervised Learning-Based Source Separation for Meeting Data
Yuang Li, Xianrui Zheng, Philip C. Woodland
Source separation can improve automatic speech recognition (ASR) under multi-party meeting scenarios by extracting single-speaker signals from overlapped speech. Despite the succes…