NDLI: Phoneme recognition using zerocrossing interval distribution of speech patterns and ANN

Content Provider	Springer Nature Link
Author	Sunil Kumar, R. K. Lajish, V. L.
Copyright Year	2012
Abstract	The speech signal is modeled using zerocrossing interval distribution of the signal in time domain. The distributions of these parameters are studied over five Malayalam (one of the most popular Indian language) vowels. We found that the distribution patterns are almost similar for repeated utterances of the same vowel and varies from vowel to vowel. These distribution patterns are used for recognizing the vowels using multilayer feed forward artificial neural network. After analyzing the distribution patterns and the vowel recognition results, we realize that the zerocrossing interval distribution parameters can be effectively used for the speech phone classification and recognition. The noise adaptness of this parameter is also studied by adding additive white Gaussian noise at different signal to noise ratio. The computational complexity of the proposed technique is also less compared to the conventional spectral techniques, which includes FFT and Cepstral methods, used in the parameterization of speech signal.
Starting Page	125
Ending Page	131
Page Count	7
File Format	PDF
ISSN	13812416
Journal	International Journal of Speech Technology
Volume Number	16
Issue Number	1
e-ISSN	15728110
Language	English
Publisher	Springer US
Publisher Date	2012-08-02
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Zerocrossing interval distribution (ZCD) Zerocrossing number (ZCN) ANN Signal, Image and Speech Processing Social Sciences Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Human-Computer Interaction Computer Vision and Pattern Recognition Software Linguistics and Language

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