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dc.contributor.authorMothukuri S.K.P.
dc.contributor.authorHegde P.
dc.contributor.authorChittaragi N.B.
dc.contributor.authorKoolagudi S.G.
dc.identifier.citation2020 International Conference on Artificial Intelligence and Signal Processing, AISP 2020 , Vol. , , p. -en_US
dc.description.abstractIn this paper, Automatic Dialect Classification (ADC) system is proposed for dialects of Kannada language (the Dravidian language spoken in Southern Karnataka). ADC system is proposed by extracting spectral Mel Frequency Cepstral Coefficients (MFCCs), and log filter bank features along with Linear predictive coefficients. In addition, prosodic pitch and energy features are extracted to capture dialect specific cues. A Kannada dialect speech corpus consisting of five prominent dialects of Kannada language is used for designing the ADC system. An attempt is made by using Artificial Neural Networks (ANNs) technique for classification of Kannada dialects. As, recently, ANNs and its variants are gaining more popularity in the area of speech processing application. Hyperparameter tuning of ANN has resulted with an increase in performance. © 2020 IEEE.en_US
dc.titleKannada Dialect Classification using Artificial Neural Networksen_US
dc.typeConference Paperen_US
Appears in Collections:2. Conference Papers

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