The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

As ChatGPT goes viral, generative AI (AIGC, aka AI-generated content) has made headlines
everywhere because of its ability to analyze and create text, images, and beyond. With such …

Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

Sparse expert models are a thirty-year old concept re-emerging as a popular architecture in
deep learning. This class of architecture encompasses Mixture-of-Experts, Switch …

Sequence-to-sequence models have been widely used in end-to-end speech processing,
for example, automatic speech recognition (ASR), speech translation (ST), and text-to …

In this study, we present recent developments on ESPnet: End-to-End Speech Processing
toolkit, which mainly involves a recently proposed architecture called Conformer …

Due to the rapid growth in network traffic and increasing security threats, Intrusion Detection
Systems (IDS) have become increasingly critical in the field of cyber security for providing …

Automatic speech recognition (ASR) has recently become an important challenge when
using deep learning (DL). It requires large-scale training datasets and high computational …

Encoder-decoder based sequence-to-sequence models have demonstrated state-of-the-art
results in end-to-end automatic speech recognition (ASR). Recently, the transformer …

We present a simple and efficient auxiliary loss function for automatic speech recognition
(ASR) based on the connectionist temporal classification (CTC) objective. The proposed …