116 citations · 808 across the 43 of their papers we have counts for
10 papers · 1 filter
Advancing Acoustic-to-Word CTC Model with Attention and Mixed-Units
Amit Das, Jinyu Li, Guoli Ye +2
The acoustic-to-word model based on the Connectionist Temporal Classification (CTC) criterion is a natural end-to-end (E2E) system directly targeting word as output unit. Two issue…
Cycle-Consistent Speech Enhancement
Zhong Meng, Jinyu Li, Yifan Gong +2
Feature mapping using deep neural networks is an effective approach for single-channel speech enhancement. Noisy features are transformed to the enhanced ones through a mapping net…
Adversarial Feature-Mapping for Speech Enhancement
Zhong Meng, Jinyu Li, Yifan Gong +2
Feature-mapping with deep neural networks is commonly used for single-channel speech enhancement, in which a feature-mapping network directly transforms the noisy features to the c…
Layer Trajectory LSTM
Jinyu Li, Changliang Liu, Yifan Gong
It is popular to stack LSTM layers to get better modeling power, especially when large amount of training data is available. However, an LSTM-RNN with too many vanilla LSTM layers…
Developing Far-Field Speaker System Via Teacher-Student Learning
Jinyu Li, Rui Zhao, Zhuo Chen +4
In this study, we develop the keyword spotting (KWS) and acoustic model (AM) components in a far-field speaker system. Specifically, we use teacher-student (T/S) learning to adapt…
Speaker-Invariant Training via Adversarial Learning
Zhong Meng, Jinyu Li, Zhuo Chen +5
We propose a novel adversarial multi-task learning scheme, aiming at actively curtailing the inter-talker feature variability while maximizing its senone discriminability so as to…
Adversarial Teacher-Student Learning for Unsupervised Domain Adaptation
Zhong Meng, Jinyu Li, Yifan Gong +2
The teacher-student (T/S) learning has been shown effective in unsupervised domain adaptation [1]. It is a form of transfer learning, not in terms of the transfer of recognition de…
Cracking the cocktail party problem by multi-beam deep attractor network
Zhuo Chen, Jinyu Li, Xiong Xiao +4
While recent progresses in neural network approaches to single-channel speech separation, or more generally the cocktail party problem, achieved significant improvement, their perf…
Advancing Acoustic-to-Word CTC Model
Jinyu Li, Guoli Ye, Amit Das +2
The acoustic-to-word model based on the connectionist temporal classification (CTC) criterion was shown as a natural end-to-end (E2E) model directly targeting words as output units…
Advancing Connectionist Temporal Classification With Attention Modeling
Amit Das, Jinyu Li, Rui Zhao +1
In this study, we propose advancing all-neural speech recognition by directly incorporating attention modeling within the Connectionist Temporal Classification (CTC) framework. In…