综述BERT与CTC Transformer在语音识别中的应用进展
Automatic Speech Recognition with BERT and CTC Transformers: A Review
- 整合BERT与CTC Transformer架构，提升语音识别性能
- 对比多篇研究结果，验证模型在不同任务上的有效性
- 适合语音识别领域研究人员和模型优化实践者
本文全面分析了基于双向编码器表示的Transformer（BERT）和连接主义时序分类（CTC）Transformer在自动语音识别（ASR）中的最新进展。首先介绍ASR的基本概念及面临的挑战，随后阐述BERT与CTC Transformer的模型架构及其在语音识别中的潜在应用。回顾多项使用这些模型进行语音识别的研究，讨论其取得的结果。此外，指出当前模型的局限性，并提出未来可能的研究方向。总体而言，该综述为对BERT与CTC Transformer在语音识别中应用感兴趣的科研人员和从业者提供了有价值的参考。
原文摘要 · Abstract (English)
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.
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