Self-attention (SA) is a critical component of Transformer neural networks that have
succeeded in automatic speech recognition (ASR). In this paper, we analyze the role of SA …

In this study, we present recent developments of models trained with the RNN-T loss in
ESPnet. It involves the use of various archi-tectures such as recently proposed Conformer …

We present a system for the Zero Resource Speech Challenge 2021, which combines a
Contrastive Predictive Coding (CPC) with deep cluster. In deep cluster, we first prepare …

In this paper, we present an incremental domain adaptation technique to prevent
catastrophic forgetting for an end-to-end automatic speech recognition (ASR) model …

Much of the recent literature on automatic speech recognition (ASR) is taking an end-to-end
approach. Unlike English where the writing system is closely related to sound, Chinese …

Transformer is widely used in natural language processing (NLP) tasks due to the parallel
and modeling of long texts. However, its performance in Chinese named entity recognition …

This study addresses the challenges of low accuracy and high computational demands in
Tibetan speech recognition by investigating the application of end-to-end networks. We …

最近, 基于自注意力的Transformer 结构在不同领域的一系列任务上表现出非常好的性能.
探索了基于Transformer 编码器和LAS (listen, attend and spell) 解码器的Transformer-LAS …

Recently, the cascaded two-pass architecture has emerged as a strong contender for on-
device automatic speech recognition (ASR). A cascade of causal and shallow non-causal …