most citedHyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models

7 citations · 15 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL20241 cited

Robust Zero-Shot Text-to-Speech Synthesis with Reverse Inference Optimization

Yuchen Hu, Chen Chen, Siyin Wang +2

In this paper, we propose reverse inference optimization (RIO), a simple and effective method designed to enhance the robustness of autoregressive-model-based zero-shot text-to-spe…

cs.CL20243 cited

It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognition

Chen Chen, Ruizhe Li, Yuchen Hu +4

Recent studies have successfully shown that large language models (LLMs) can be successfully used for generative error correction (GER) on top of the automatic speech recognition (…

cs.CL20243 cited

Large Language Models are Efficient Learners of Noise-Robust Speech Recognition

Yuchen Hu, Chen Chen, Chao-Han Huck Yang +4

Recent advances in large language models (LLMs) have promoted generative error correction (GER) for automatic speech recognition (ASR), which leverages the rich linguistic knowledg…

cs.CL20231 cited

Generative error correction for code-switching speech recognition using large language models

Chen Chen, Yuchen Hu, Chao-Han Huck Yang +3

Code-switching (CS) speech refers to the phenomenon of mixing two or more languages within the same sentence. Despite the recent advances in automatic speech recognition (ASR), CS-…

cs.CL20237 cited

HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models

Chen Chen, Yuchen Hu, Chao-Han Huck Yang +3

Advancements in deep neural networks have allowed automatic speech recognition (ASR) systems to attain human parity on several publicly available clean speech datasets. However, ev…