Quantitative Analysis of Feature Extraction Techniques for Isolated Word Recognition-Reference-Cited by-同舟云学术

Quantitative Analysis of Feature Extraction Techniques for Isolated Word Recognition

Published:2019 Issue: Volume: Page:618-627
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

Agarwal Chesta,Chakraborty Pinaki,Barai Serena,Goyal Vaibhav

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-13-9942-8_58

Reference14 articles.

1. Chuctaya, H.F.C., Mercado, R.N.M., Gaona, J.J.G.: Isolated automatic speech recognition of Quechua numbers using MFCC, DTW and KNN. Int. J. Adv. Comput. Sci. Appl. 9, 24–29 (2018)

2. Dhingra, S.D., Nijhawan, G., Pandit, P.: Isolated speech recognition using MFCC and DTW. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 2, 4085–4092 (2013)

3. Gedam, Y.K., Magare, S.S., Dabhade, A.C., Deshmukh, R.R.: Development of automatic speech recognition of Marathi numerals - a review. Int. J. Eng. Innov. Technol. 3, 198–203 (2014)

4. Ghadee, A., Jonvale, G.B., Deshmukh, R.R.: Speech feature extraction using Mel- frequency cepstral coefficient (MFCC). In: Proceedings of Conference on Emerging Treads in Computer Science, Communication and Information Technology, pp. 503–506 (2010)

5. Gulaker, V.: Speech recognition by human and machine. M.Sc. Thesis, Norwegian University of Science and Technology (2010)

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