Phoneme recognition using zerocrossing interval distribution of speech patterns and ANN

Authors:
R. K. Sunil Kumar;V. L. Lajish
Affiliations:
Department of Computer Science, University of Calicut, Kerala, India 673635;Department of Computer Science, University of Calicut, Kerala, India 673635
Venue:
International Journal of Speech Technology
Year:
2013

Citing 3
Cited 0

Neurocomputing

Neurocomputing
Fundamentals of speech recognition

Fundamentals of speech recognition
Zero-crossing based spectral analysis and SVD spectral analysis forformant frequency estimation in noise

IEEE Transactions on Signal Processing

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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.