Automatic Speech Recognition with BERT and CTC Transformers: A Review

TL;DR

This review analyzes recent advances in automatic speech recognition using BERT and CTC transformers, highlighting their architectures, applications, results, limitations, and future research directions.

Contribution

It provides a comprehensive overview of how BERT and CTC transformers are applied to ASR, including recent studies, results, and research gaps.

Findings

BERT and CTC transformers improve speech recognition accuracy.

Several studies demonstrate the effectiveness of these models in ASR tasks.

Limitations include computational complexity and data requirements.

Abstract

This review paper provides a comprehensive analysis of recent advances in automatic speech recognition (ASR) with bidirectional encoder representations from transformers BERT and connectionist temporal classification (CTC) transformers. The paper first introduces the fundamental concepts of ASR and discusses the challenges associated with it. It then explains the architecture of BERT and CTC transformers and their potential applications in ASR. The paper reviews several studies that have used these models for speech recognition tasks and discusses the results obtained. Additionally, the paper highlights the limitations of these models and outlines potential areas for further research. All in all, this review provides valuable insights for researchers and practitioners who are interested in ASR with BERT and CTC transformers.