Artificial Intelligence and Communication Technologies
Automatic Hindi Speech Recognition: Challenges and Future Scop
Authors: Ankit kumar, Subhash Chandra Gupta, Shashwat Tripathi, Vanshika Lamba and Aditya Singh
Publishing Date: 04-06-2023
ISBN: 978-81-955020-5-9
Abstract
Automatic Speech Recognition (ASR) field is the witness of remarkable improvement over the last few years. Deep learning architectures are the main reason for ASR evaluation. Deep learning techniques tremendously improve ASR performance, but these architectures are data-hungry. There are more than 5000 languages in India that do not have such a huge amount of resources. Hindi is one of them. The lack of freely available Large Vocabulary Hindi speech datasets is the major hurdle for developing Hindi ASR. In this paper, we investigate the various work available for Hindi ASR. The available resource is also discussed with issues and challenges in Hindi ASR. This paper provides a detailed analysis of various techniques available in ASR with comparison to Hindi ASR. The future scope of Hindi ASR is also presented.
Keywords
Perpetual, Recognition, Neural, Spectral, Convolution.
Cite as
Ankit kumar, Subhash Chandra Gupta, Shashwat Tripathi, Vanshika Lamba and Aditya Singh, "Automatic Hindi Speech Recognition: Challenges and Future Scop", In: Saroj Hiranwal and Garima Mathur (eds), Artificial Intelligence and Communication Technologies, SCRS, India, 2023, pp. 1113-1122. https://doi.org/10.52458/978-81-955020-5-9-105