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 …

Recurrent neural networks are effective models to process sequences. However, they are
unable to learn long-term dependencies because of their inherent sequential nature. As a …

Visual speech recognition (VSR) aims to recognize the content of speech based on lip
movements, without relying on the audio stream. Advances in deep learning and the …

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 …

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

How to leverage dynamic contextual information in end-to-end speech recognition has
remained an active research area. Previous solutions to this problem were either designed …

End-to-end models in general, and Recurrent Neural Network Transducer (RNN-T) in
particular, have gained significant traction in the automatic speech recognition community in …

The problem of machine learning systems demonstrating bias towards specific groups of
individuals has been studied extensively, particularly in the Facial Recognition area, but …

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 …

State space models (SSMs) have recently shown promising results on small-scale sequence
and language modelling tasks, rivalling and outperforming many attention-based …