Mel-Frequency Cepstral Coefficient Features Based on Standard Deviation and Principal Component Analysis for Language Identification Systems-Reference-Cited by-同舟云学术

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Mel-Frequency Cepstral Coefficient Features Based on Standard Deviation and Principal Component Analysis for Language Identification Systems

Published:2021-07-16 Issue:5 Volume:13 Page:1136-1153
ISSN:1866-9956
Container-title:Cognitive Computation
language:en
Short-container-title:Cogn Comput

Author:

Albadr Musatafa Abbas Abbood,Tiun Sabrina,Ayob Masri,Mohammed Manal,AL-Dhief Fahad Taha

Publisher

Springer Science and Business Media LLC

Subject

Cognitive Neuroscience,Computer Science Applications,Computer Vision and Pattern Recognition

Link

https://link.springer.com/content/pdf/10.1007/s12559-021-09914-w.pdf

Reference53 articles.

1. Lee KA, et al. The 2015 NIST Language Recognition Evaluation: the Shared View of I2R, Fantastic4 and SingaMS. in Interspeech. 2016.

2. Garg A, Gupta V, Jindal M. A survey of language identification techniques and applications. J Emerg Technol Web Intell. 2014;6(4):388–400.

3. Li J, et al. LSTM time and frequency recurrence for automatic speech recognition. in Automatic Speech Recognition and Understanding (ASRU), 2015 IEEE Workshop on. 2015. IEEE.

4. Hafen RP, Henry MJ. Speech information retrieval: a review. Multimedia Syst. 2012;18(6):499–518.

5. Albadr MAA, et al. Spoken language identification based on the enhanced self-adjusting extreme learning machine approach. PloS one, 2018. 13(4): p. e0194770.

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