Recognition of repetition and prolongation in stuttered speech using ANN

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dc.contributor.authorSavin, P.S.
dc.contributor.authorRamteke, P.B.
dc.contributor.authorKoolagudi, S.G.
dc.date.accessioned2020-03-30T10:23:01Z
dc.date.available2020-03-30T10:23:01Z
dc.date.issued2016
dc.description.abstractThis paper mainly focuses on repetition and prolongation detection in stuttered speech signal. The acoustic and pitch related features like Mel-frequency cepstral coefficients (MFCCs), formants, pitch, zero crossing rate (ZCR) and Energy are used to test the effectiveness in recognizing repetitions and prolongations in stammered speech. Artificial Neural Networks (ANN) are used as classifier. The results are evaluated using combination of different features. The results show that the ANN classifier trained using MFCC features achieves an average accuracy of 87.39% for repetition and prolongation recognition. � Springer India 2016.en_US
dc.identifier.citationSmart Innovation, Systems and Technologies, 2016, Vol.43, , pp.65-71en_US
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/8916
dc.titleRecognition of repetition and prolongation in stuttered speech using ANNen_US
dc.typeBook chapteren_US

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