Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

We present a structured overview of adaptation algorithms for neural network-based speech
recognition, considering both hybrid hidden Markov model/neural network systems and end …

Automatic speech recognition (ASR) has recently become an important challenge when
using deep learning (DL). It requires large-scale training datasets and high computational …

Catastrophic forgetting is a problem of neural networks that loses the information of the first
task after training the second task. Here, we propose a method, ie incremental moment …

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 enhance …

This work presents a broad study on the adaptation of neural network acoustic models by
means of learning hidden unit contributions (LHUC)—a method that linearly re-combines …

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 …

This paper investigates the use of parameterised sigmoid and rectified linear unit (ReLU)
hidden activation functions in deep neural network (DNN) speaker adaptation. The sigmoid …

We propose a novel approach to addressing the adaptation effectiveness issue in parameter
adaptation for deep neural network (DNN) based acoustic models for automatic speech …

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 a …