Download pdf
Automatic speech recognition technology: History, applications and improvements
- 1 Beijing Forestry University
* Author to whom correspondence should be addressed.
Abstract
In today’s world, automatic speech recognition(ASR) has been an important part of artificial intelligence. It has been recognized as an extremely difficult highly challenging high-tech topic. It mainly converts the vocabulary content in human speech into computer-readable input, which is generally understandable text content, and may also be binary encoding or character sequences. Since the 1950s, ASR has been continuously developing from simple systems for pronunciation of 10 English numbers to the rise of multiple frameworks and different neural networks. The process of ASR is constantly becoming diversified and specialized. Based on the analysis of existing literature, this article will briefly describe the history of speech recognition technology, the current development status of speech recognition, various applications in daily life and advanced areas, and methods for improvements. It indicates that nowadays automatic technology has become an essential part in people’s daily lives. Simple methods for eliminating echoes and noise to improve system performance and user experience are also an important part that should be considered in the use of ASR.
Keywords
Automatic speech recognition, Markov model, improvement
[1]. Juang, B. H., & Rabiner, L. R. (2005). Automatic speech recognition–a brief history of the technology development. Georgia Institute of Technology. Atlanta Rutgers University and the University of California. Santa Barbara, 1, 67.
[2]. Miao Miao & HaiWu Ma.(2006). Application of HMM in Automatic Speech Recognition System. Modern Electronics Technique(16),64-66.
[3]. Wang, D., Wang, X., & Lv, S. (2019). An overview of end-to-end automatic speech recognition. Symmetry, 11(8), 1018.
[4]. XiangZhi He.(2002).The Research and Development of Speech Recognition. Computer and Modernization(03), 3-6.
[5]. Pei Ding.(2004). Noise Robust Technologies in Speech Recognition(Dissertation Submitted to Tsinghua University in partial fulfillment of the requirement for the degree of Doctor of Engineering).https://kns.cnki.net/kcms2/article/abstract?v=2F6201taHdcUwBjYSMP8SjmKHOTnRx5SwH8_3kv5Ng_nb-S1Vu5Y8YfFRVyK7Po26Yco0xAnHYKrZsZxBOMOSG4LHFw0xe5qR9xk5JnrqZUFNtOPQWGjNSfWqVPafDKR&uniplatform=NZKPT&language=CHS
[6]. Errattahi, R., El Hannani, A., & Ouahmane, H. (2018). Automatic speech recognition errors detection and correction: A review. Procedia Computer Science, 128, 32-37.
Cite this article
Liang,Q. (2024). Automatic speech recognition technology: History, applications and improvements. Applied and Computational Engineering,65,180-184.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of Urban Intelligence: Machine Learning in Smart City Solutions - CONFSEML 2024
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).