10 citations · 26 across the 9 of their papers we have counts for
6 papers · 1 filter
Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition
Chao-Han Huck Yang, Taejin Park, Yuan Gong +18
Given recent advances in generative AI technology, a key question is how large language models (LLMs) can enhance acoustic modeling tasks using text decoding results from a frozen,…
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…
Enhancing Zero-shot Text-to-Speech Synthesis with Human Feedback
Chen Chen, Yuchen Hu, Wen Wu +3
In recent years, text-to-speech (TTS) technology has witnessed impressive advancements, particularly with large-scale training datasets, showcasing human-level speech quality and i…
Self-Taught Recognizer: Toward Unsupervised Adaptation for Speech Foundation Models
Yuchen Hu, Chen Chen, Chao-Han Huck Yang +4
We propose an unsupervised adaptation framework, Self-TAught Recognizer (STAR), which leverages unlabeled data to enhance the robustness of automatic speech recognition (ASR) syste…
Bayesian Example Selection Improves In-Context Learning for Speech, Text, and Visual Modalities
Siyin Wang, Chao-Han Huck Yang, Ji Wu +1
Large language models (LLMs) can adapt to new tasks through in-context learning (ICL) based on a few examples presented in dialogue history without any model parameter update. Desp…
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…