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 …

We present a state-of-the-art speech recognition system developed using end-to-end deep
learning. Our architecture is significantly simpler than traditional speech systems, which rely …

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

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 …

Current state-of-the-art speech recognition systems build on recurrent neural networks for
acoustic and/or language modeling, and rely on feature extraction pipelines to extract mel …

While artificial neural networks have been in existence for over half a century, it was not until
year 2010 that they had made a significant impact on speech recognition with a deep form of …

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 …

Recent advancements in deep learning (DL) have posed a significant challenge for
automatic speech recognition (ASR). ASR relies on extensive training datasets, including …

Automatic Speech Recognition (ASR), which is aimed to enable natural human–machine
interaction, has been an intensive research area for decades. Many core technologies, such …

With the widespread adoption of deep learning, natural language processing (NLP), and
speech applications in various domains such as finance, healthcare, and government and …