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 …

Speech technology is not appropriately explored even though modern advances in speech
technology—especially those driven by deep learning (DL) technology—offer …

SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to facilitate the
research and development of neural speech processing technologies by being simple …

Recently Transformer and Convolution neural network (CNN) based models have shown
promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural …

Recently great strides have been made in the field of automatic speech recognition (ASR) by
using various deep learning techniques. In this study, we present a thorough comparison …

The recently proposed Conformer model has become the de facto backbone model for
various downstream speech tasks based on its hybrid attention-convolution architecture that …

Convolutional neural networks (CNN) have shown promising results for end-to-end speech
recognition, albeit still behind other state-of-the-art methods in performance. In this paper …

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

In this paper, we propose TitaNet, a novel neural network architecture for extracting speaker
representations. We employ 1D depth-wise separable convolutions with Squeeze-and …

Abstract End-to-end Automatic Speech Recognition (ASR) systems based on neural
networks have seen large improvements in recent years. The availability of large scale hand …