477 citations · 1.7k across the 84 of their papers we have counts for
16 papers · 1 filter
Disambiguation of Chinese Polyphones in an End-to-End Framework with Semantic Features Extracted by Pre-trained BERT
Dongyang Dai, Zhiyong Wu, Shiyin Kang +5
Grapheme-to-phoneme (G2P) conversion serves as an essential component in Chinese Mandarin text-to-speech (TTS) system, where polyphone disambiguation is the core issue. In this pap…
Audio-visual Multi-channel Recognition of Overlapped Speech
Jianwei Yu, Bo Wu, Rongzhi Gu +7
Automatic speech recognition (ASR) of overlapped speech remains a highly challenging task to date. To this end, multi-channel microphone array data are widely used in state-of-the-…
Neural Spatio-Temporal Beamformer for Target Speech Separation
Yong Xu, Meng Yu, Shi-Xiong Zhang +4
Purely neural network (NN) based speech separation and enhancement methods, although can achieve good objective scores, inevitably cause nonlinear speech distortions that are harmf…
Enhancing End-to-End Multi-channel Speech Separation via Spatial Feature Learning
Rongzhi Gu, Shi-Xiong Zhang, Lianwu Chen +5
Hand-crafted spatial features (e.g., inter-channel phase difference, IPD) play a fundamental role in recent deep learning based multi-channel speech separation (MCSS) methods. Howe…
Multi-modal Multi-channel Target Speech Separation
Rongzhi Gu, Shi-Xiong Zhang, Yong Xu +3
Target speech separation refers to extracting a target speaker's voice from an overlapped audio of simultaneous talkers. Previously the use of visual modality for target speech sep…
Audio-visual Recognition of Overlapped speech for the LRS2 dataset
Jianwei Yu, Shi-Xiong Zhang, Jian Wu +7
Automatic recognition of overlapped speech remains a highly challenging task to date. Motivated by the bimodal nature of human speech perception, this paper investigates the use of…
Overlapped speech recognition from a jointly learned multi-channel neural speech extraction and representation
Bo Wu, Meng Yu, Lianwu Chen +3
We propose an end-to-end joint optimization framework of a multi-channel neural speech extraction and deep acoustic model without mel-filterbank (FBANK) extraction for overlapped s…
DFSMN-SAN with Persistent Memory Model for Automatic Speech Recognition
Zhao You, Dan Su, Jie Chen +2
Self-attention networks (SAN) have been introduced into automatic speech recognition (ASR) and achieved state-of-the-art performance owing to its superior ability in capturing long…
Mixup-breakdown: a consistency training method for improving generalization of speech separation models
Max W. Y. Lam, Jun Wang, Dan Su +1
Deep-learning based speech separation models confront poor generalization problem that even the state-of-the-art models could abruptly fail when evaluating them in mismatch conditi…
Audio-Visual Speech Separation and Dereverberation with a Two-Stage Multimodal Network
Ke Tan, Yong Xu, Shi-Xiong Zhang +2
Background noise, interfering speech and room reverberation frequently distort target speech in real listening environments. In this study, we address joint speech separation and d…
Maximizing Mutual Information for Tacotron
Peng Liu, Xixin Wu, Shiyin Kang +3
End-to-end speech synthesis methods already achieve close-to-human quality performance. However compared to HMM-based and NN-based frame-to-frame regression methods, they are prone…
Teach an all-rounder with experts in different domains
Zhao You, Dan Su, Dong Yu
In many automatic speech recognition (ASR) tasks, an ideal model has to be applicable over multiple domains. In this paper, we propose to teach an all-rounder with experts in diffe…
Improved Speaker-Dependent Separation for CHiME-5 Challenge
Jian Wu, Yong Xu, Shi-Xiong Zhang +4
This paper summarizes several follow-up contributions for improving our submitted NWPU speaker-dependent system for CHiME-5 challenge, which aims to solve the problem of multi-chan…
Time Domain Audio Visual Speech Separation
Jian Wu, Yong Xu, Shi-Xiong Zhang +4
Audio-visual multi-modal modeling has been demonstrated to be effective in many speech related tasks, such as speech recognition and speech enhancement. This paper introduces a new…
Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching
Chih-Kuan Yeh, Jianshu Chen, Chengzhu Yu +1
We consider the problem of training speech recognition systems without using any labeled data, under the assumption that the learner can only access to the input utterances and a p…
Recent Progresses in Deep Learning based Acoustic Models (Updated)
Dong Yu, Jinyu Li
In this paper, we summarize recent progresses made in deep learning based acoustic models and the motivation and insights behind the surveyed techniques. We first discuss acoustic…