Automatic speech recognition (ASR) is a crucial technology for man-machine interaction.
End-to-end models have been studied recently in deep learning for ASR. However, these …

In recent years, there has been a great deal of research in developing end-to-end speech
recognition models, which enable simplifying the traditional pipeline and achieving …

Although large foundation models pre-trained by self-supervised learning have achieved
state-of-the-art performance in many tasks including automatic speech recognition (ASR) …

This paper is the first study to apply deep mutual learning (DML) to end-to-end ASR models.
In DML, multiple models are trained simultaneously and collaboratively by mimicking each …

Most automatic speech recognition (ASR) neural network models are not suitable for mobile
devices due to their large model sizes. Therefore, it is required to reduce the model size to …

Large-scale pre-trained language models (PLMs) have shown great potential in natural
language processing tasks. Leveraging the capabilities of PLMs to enhance automatic …

Knowledge distillation has been widely used to compress existing deep learning models
while preserving the performance on a wide range of applications. In the specific context of …

Recently, the advance in deep learning has brought a considerable improvement in the end-
to-end speech recognition field, simplifying the traditional pipeline while producing …

Knowledge distillation is an effective machine learning technique to transfer knowledge from
a teacher model to a smaller student model, especially with unlabeled data. In this paper, we …

In this paper, we propose an incremental learning method for end-to-end Automatic Speech
Recognition (ASR) which enables an ASR system to perform well on new tasks while …