Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Kannada language

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    Kannada Dialect Classification using Artificial Neural Networks
    (Institute of Electrical and Electronics Engineers Inc., 2020) Mothukuri, S.K.P.; Hegde, P.; Chittaragi, N.B.; Koolagudi, S.G.
    In 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.
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    Monophone and Triphone Acoustic Phonetic Model for Kannada Speech Recognition System
    (Institute of Electrical and Electronics Engineers Inc., 2022) Kumar, T.N.M.; Jayan, A.; Bhat, S.; Anvith, M.; Narasimhadhan, A.V.
    The automatic Speech Recognition system (ASR) is the most widely used application in the speech domain. ASR systems generate text data from spoken utterances without manual intervention. In this work, we build an ASR system for the Kannada language. For building the proposed system, we extract Mel Frequency Cepstral Coefficients (MFCC) features from the audio data, and the Kannada language model is developed using corresponding labels. The dictionary generation and phonetic labelings are automated. Recognition performance is compared for both monophonic and triphone models. The word error rate of 15.73 % and the sentence error rate of 55.5 % are achieved for the triphone model. Comparatively, the triphone model gives a better performance than the monophonic model. © 2022 IEEE.