10 citations · 44 across the 35 of their papers we have counts for
14 papers · 1 filter
From KAN to GR-KAN: Advancing Speech Enhancement with KAN-Based Methodology
Haoyang Li, Yuchen Hu, Chen Chen +3
Deep neural network (DNN)-based speech enhancement (SE) usually uses conventional activation functions, which lack the expressiveness to capture complex multiscale structures neede…
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,…
Video-to-Audio Generation with Fine-grained Temporal Semantics
Yuchen Hu, Yu Gu, Chenxing Li +2
With recent advances of AIGC, video generation have gained a surge of research interest in both academia and industry (e.g., Sora). However, it remains a challenge to produce tempo…
SSR-Speech: Towards Stable, Safe and Robust Zero-shot Text-based Speech Editing and Synthesis
Helin Wang, Meng Yu, Jiarui Hai +5
In this paper, we introduce SSR-Speech, a neural codec autoregressive model designed for stable, safe, and robust zero-shot textbased speech editing and text-to-speech synthesis. S…
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…
Overcoming Catastrophic Forgetting by Exemplar Selection in Task-oriented Dialogue System
Chen Chen, Ruizhe Li, Yuchen Hu +3
Intelligent task-oriented dialogue systems (ToDs) are expected to continuously acquire new knowledge, also known as Continual Learning (CL), which is crucial to fit ever-changing u…
Listen Again and Choose the Right Answer: A New Paradigm for Automatic Speech Recognition with Large Language Models
Yuchen Hu, Chen Chen, Chengwei Qin +3
Recent advances in large language models (LLMs) have promoted generative error correction (GER) for automatic speech recognition (ASR), which aims to predict the ground-truth trans…
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 (…
GenTranslate: Large Language Models are Generative Multilingual Speech and Machine Translators
Yuchen Hu, Chen Chen, Chao-Han Huck Yang +4
Recent advances in large language models (LLMs) have stepped forward the development of multilingual speech and machine translation by its reduced representation errors and incorpo…
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…
Cross-Modality and Within-Modality Regularization for Audio-Visual DeepFake Detection
Heqing Zou, Meng Shen, Yuchen Hu +3
Audio-visual deepfake detection scrutinizes manipulations in public video using complementary multimodal cues. Current methods, which train on fused multimodal data for multimodal…
Multichannel AV-wav2vec2: A Framework for Learning Multichannel Multi-Modal Speech Representation
Qiushi Zhu, Jie Zhang, Yu Gu +2
Self-supervised speech pre-training methods have developed rapidly in recent years, which show to be very effective for many near-field single-channel speech tasks. However, far-fi…