Conformer, combining convolution and self-attention sequentially to capture both local and
global information, has shown remarkable performance and is currently regarded as the …

Artificial Neural Networks (ANNs) were created inspired by the neural networks in the
human brain and have been widely applied in speech processing. The application areas of …

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 …

Recently, several studies reported that dot-product self-attention (SA) may not be
indispensable to the state-of-the-art Transformer models. Motivated by the fact that dense …

Recently, conformer-based end-to-end automatic speech recognition, which outperforms
recurrent neural network based ones, has received much attention. Although the parallel …

Speech applications in far-field real world settings often deal with signals that are corrupted
by reverberation. The task of dereverberation constitutes an important step to improve the …

The concept of multi-headed self attention (MHSA) introduced as a critical building block of a
Transformer Encoder/Decoder Module has made a significant impact in the areas of natural …

Human motion prediction is a crucial step for achieving human-robot interactions. While
recent transformer-based methods have shown great potentials in 3D human motion …

Transformer-based end-to-end speech recognition models have received considerable
attention in recent years due to their high training speed and ability to model a long-range …

Keyword Spotting (KWS) from speech signals is widely applied to perform fully hands-free
speech recognition. The KWS network is designed as a small-footprint model so it can …