92 citations · 523 across the 22 of their papers we have counts for
6 papers · 1 filter
Online Continual Learning of End-to-End Speech Recognition Models
Muqiao Yang, Ian Lane, Shinji Watanabe
Continual Learning, also known as Lifelong Learning, aims to continually learn from new data as it becomes available. While prior research on continual learning in automatic speech…
Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention
Ming Zeng, Haoxiang Gao, Tong Yu +4
Deep neural networks, including recurrent networks, have been successfully applied to human activity recognition. Unfortunately, the final representation learned by recurrent netwo…
Semi-Supervised Convolutional Neural Networks for Human Activity Recognition
Ming Zeng, Tong Yu, Xiao Wang +3
Labeled data used for training activity recognition classifiers are usually limited in terms of size and diversity. Thus, the learned model may not generalize well when used in rea…
Recurrent Models for Auditory Attention in Multi-Microphone Distance Speech Recognition
Suyoun Kim, Ian Lane
Integration of multiple microphone data is one of the key ways to achieve robust speech recognition in noisy environments or when the speaker is located at some distance from the i…
Transferring Knowledge from a RNN to a DNN
William Chan, Nan Rosemary Ke, Ian Lane
Deep Neural Network (DNN) acoustic models have yielded many state-of-the-art results in Automatic Speech Recognition (ASR) tasks. More recently, Recurrent Neural Network (RNN) mode…
Deep Recurrent Neural Networks for Acoustic Modelling
William Chan, Ian Lane
We present a novel deep Recurrent Neural Network (RNN) model for acoustic modelling in Automatic Speech Recognition (ASR). We term our contribution as a TC-DNN-BLSTM-DNN model, the…