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 …

Federated learning (FL) is a heavily promoted approach for training ML models on sensitive
data, eg, text typed by users on their smartphones. FL is expressly designed for training on …

Multilingual end-to-end (E2E) models have shown great promise in expansion of automatic
speech recognition (ASR) coverage of the world's languages. They have shown …

The external language models (LM) integration remains a challenging task for end-to-end
(E2E) automatic speech recognition (ASR) which has no clear division between acoustic …

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 …

Sequence-to-sequence models provide a simple and elegant solution for building speech
recognition systems by folding separate components of a typical system, namely acoustic …

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 …

The variety of accents has posed a big challenge to speech recognition. The Accented
English Speech Recognition Challenge (AESRC2020) is designed for providing a common …

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 efficacy of external language model (LM) integration with existing end-to-end (E2E)
automatic speech recognition (ASR) systems can be improved significantly using the …