1 citations · 3 across the 14 of their papers we have counts for
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Efficient Scaling for LLM-based ASR
Bingshen Mu, Yiwen Shao, Kun Wei +2
Large language model (LLM)-based automatic speech recognition (ASR) achieves strong performance but often incurs high computational costs. This work investigates how to obtain the…
Hearing More with Less: Multi-Modal Retrieval-and-Selection Augmented Conversational LLM-Based ASR
Bingshen Mu, Hexin Liu, Hongfei Xue +2
Automatic Speech Recognition (ASR) aims to convert human speech content into corresponding text. In conversational scenarios, effectively utilizing context can enhance its accuracy…
Mixture of LoRA Experts with Multi-Modal and Multi-Granularity LLM Generative Error Correction for Accented Speech Recognition
Bingshen Mu, Kun Wei, Pengcheng Guo +1
Despite improvements in automatic speech recognition, performance drops with accented speech. Generative error correction (GER) leverages the linguistic knowledge of large language…
OSUM: Advancing Open Speech Understanding Models with Limited Resources in Academia
Xuelong Geng, Kun Wei, Qijie Shao +18
Large Language Models (LLMs) have made significant progress in various downstream tasks, inspiring the development of Speech Understanding Language Models (SULMs) to enable compreh…
DQ-Data2vec: Decoupling Quantization for Multilingual Speech Recognition
Qijie Shao, Linhao Dong, Kun Wei +2
Data2vec is a self-supervised learning (SSL) approach that employs a teacher-student architecture for contextual representation learning via masked prediction, demonstrating remark…
CAMEL: Cross-Attention Enhanced Mixture-of-Experts and Language Bias for Code-Switching Speech Recognition
He Wang, Xucheng Wan, Naijun Zheng +4
Code-switching automatic speech recognition (ASR) aims to transcribe speech that contains two or more languages accurately. To better capture language-specific speech representatio…
HDMoLE: Mixture of LoRA Experts with Hierarchical Routing and Dynamic Thresholds for Fine-Tuning LLM-based ASR Models
Bingshen Mu, Kun Wei, Qijie Shao +2
Recent advancements in integrating Large Language Models (LLM) with automatic speech recognition (ASR) have performed remarkably in general domains. While supervised fine-tuning (S…
Unveiling the Potential of LLM-Based ASR on Chinese Open-Source Datasets
Xuelong Geng, Tianyi Xu, Kun Wei +9
Large Language Models (LLMs) have demonstrated unparalleled effectiveness in various NLP tasks, and integrating LLMs with automatic speech recognition (ASR) is becoming a mainstrea…
E-chat: Emotion-sensitive Spoken Dialogue System with Large Language Models
Hongfei Xue, Yuhao Liang, Bingshen Mu +4
This study focuses on emotion-sensitive spoken dialogue in human-machine speech interaction. With the advancement of Large Language Models (LLMs), dialogue systems can handle multi…
SALT: Distinguishable Speaker Anonymization Through Latent Space Transformation
Yuanjun Lv, Jixun Yao, Peikun Chen +3
Speaker anonymization aims to conceal a speaker's identity without degrading speech quality and intelligibility. Most speaker anonymization systems disentangle the speaker represen…
BA-MoE: Boundary-Aware Mixture-of-Experts Adapter for Code-Switching Speech Recognition
Peikun Chen, Fan Yu, Yuhao Lian +5
Mixture-of-experts based models, which use language experts to extract language-specific representations effectively, have been well applied in code-switching automatic speech reco…