Deep Learning in Automatic Speech Recognition (ASR): A Review
- DOI
- 10.2991/978-2-494069-51-0_23How to use a DOI?
- Keywords
- Deep Learning; Automatic Speech Recognition; CNN; LAS
- Abstract
In today's big data era, many traditional machine learning algorithms for processing large amount of raw unlabeled speech data are no longer applicable. At the same time, deep learning models, with their powerful modeling capability for massive data, are able to process unlabeled data directly and have become a hot research topic in the field of automatic speech recognition. The paper gives an overview of the application of deep learning in speech recognition. The research results of deep learning in the field of speech recognition in recent years are introduced, the correlation between traditional speech recognition models and the current deep learning models is discussed, the development trend of deep learning in the field of speech recognition is analyzed, and it is pointed out that deep learning models need to absorb the ideas of traditional speech recognition models in order to better build a speech recognition system based on deep learning models.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Taiyao Zeng PY - 2022 DA - 2022/12/09 TI - Deep Learning in Automatic Speech Recognition (ASR): A Review BT - Proceedings of the 2022 7th International Conference on Modern Management and Education Technology (MMET 2022) PB - Atlantis Press SP - 173 EP - 179 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-494069-51-0_23 DO - 10.2991/978-2-494069-51-0_23 ID - Zeng2022 ER -