10 citations · 44 across the 35 of their papers we have counts for
13 papers · 1 filter
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-…
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…
Rep2wav: Noise Robust text-to-speech Using self-supervised representations
Qiushi Zhu, Yu Gu, Rilin Chen +4
Benefiting from the development of deep learning, text-to-speech (TTS) techniques using clean speech have achieved significant performance improvements. The data collected from rea…
Noise-aware Speech Enhancement using Diffusion Probabilistic Model
Yuchen Hu, Chen Chen, Ruizhe Li +2
With recent advances of diffusion model, generative speech enhancement (SE) has attracted a surge of research interest due to its great potential for unseen testing noises. However…
MIR-GAN: Refining Frame-Level Modality-Invariant Representations with Adversarial Network for Audio-Visual Speech Recognition
Yuchen Hu, Chen Chen, Ruizhe Li +2
Audio-visual speech recognition (AVSR) attracts a surge of research interest recently by leveraging multimodal signals to understand human speech. Mainstream approaches addressing…
Hearing Lips in Noise: Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech Recognition
Yuchen Hu, Ruizhe Li, Chen Chen +3
Audio-visual speech recognition (AVSR) provides a promising solution to ameliorate the noise-robustness of audio-only speech recognition with visual information. However, most exis…
A Neural State-Space Model Approach to Efficient Speech Separation
Chen Chen, Chao-Han Huck Yang, Kai Li +3
In this work, we introduce S4M, a new efficient speech separation framework based on neural state-space models (SSM). Motivated by linear time-invariant systems for sequence modeli…
Eeg2vec: Self-Supervised Electroencephalographic Representation Learning
Qiushi Zhu, Xiaoying Zhao, Jie Zhang +3
Recently, many efforts have been made to explore how the brain processes speech using electroencephalographic (EEG) signals, where deep learning-based approaches were shown to be a…
UniS-MMC: Multimodal Classification via Unimodality-supervised Multimodal Contrastive Learning
Heqing Zou, Meng Shen, Chen Chen +3
Multimodal learning aims to imitate human beings to acquire complementary information from multiple modalities for various downstream tasks. However, traditional aggregation-based…
Cross-Modal Global Interaction and Local Alignment for Audio-Visual Speech Recognition
Yuchen Hu, Ruizhe Li, Chen Chen +3
Audio-visual speech recognition (AVSR) research has gained a great success recently by improving the noise-robustness of audio-only automatic speech recognition (ASR) with noise-in…
Noise-Aware Speech Separation with Contrastive Learning
Zizheng Zhang, Chen Chen, Hsin-Hung Chen +3
Recently, speech separation (SS) task has achieved remarkable progress driven by deep learning technique. However, it is still challenging to separate target speech from noisy mixt…
Metric-oriented Speech Enhancement using Diffusion Probabilistic Model
Chen Chen, Yuchen Hu, Weiwei Weng +1
Deep neural network based speech enhancement technique focuses on learning a noisy-to-clean transformation supervised by paired training data. However, the task-specific evaluation…
Unsupervised Noise adaptation using Data Simulation
Chen Chen, Yuchen Hu, Heqing Zou +2
Deep neural network based speech enhancement approaches aim to learn a noisy-to-clean transformation using a supervised learning paradigm. However, such a trained-well transformati…