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 …

End-to-end (E2E) automatic speech recognition (ASR) models have recently demonstrated
superior performance over the traditional hybrid ASR models. Training an E2E ASR model …

Recently, data-driven based Automatic Speech Recognition (ASR) systems have achieved
state-of-the-art results. And transfer learning is often used when those existing systems are …

In this paper, we present Adaptive Computation Steps (ACS) algorithm, which enables end-
to-end speech recognition models to dynamically decide how many frames should be …

Automatic speech recognition (ASR) technologies today are primarily optimized for given
datasets; thus, any changes in the application environment (eg, acoustic conditions or topic …

In this paper, we demonstrate the efficacy of transfer learning and continuous learning for
various automatic speech recognition (ASR) tasks using end-to-end models trained with …

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 …

This paper addresses end-to-end automatic speech recognition (ASR) for long audio
recordings such as lecture and conversational speeches. Most end-to-end ASR models are …

A new system architecture for end-to-end automatic speech recognition (ASR) is proposed
that combines the alignment capabilities of the connectionist temporal classification (CTC) …

Recently, self-supervised pretraining has achieved impressive results in end-to-end (E2E)
automatic speech recognition (ASR). However, the dominant sequence-to-sequence (S2S) …