Conference Papers

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

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Author: search.filters.author.Koolagudi, S.G.Subject: search.filters.subject.Dialect Classification

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    Acoustic features based word level dialect classification using SVM and ensemble methods
    (Institute of Electrical and Electronics Engineers Inc., 2017) Chittaragi, N.B.; Koolagudi, S.G.
    In this paper, word based dialect classification system is proposed by using acoustic characteristics of the speech signal. Dialects mainly represent the different pronunciation patterns of any language. Dialectal cues can exist at various levels such as phoneme, syllable, word, sentence and phrase in an utterance. Word level dialectal traits are extracted to recognize dialects since every word exhibits significant dialect discriminating cues. Intonational Variations in English (IViE) speech corpus recorded in British English has been considered. The corpus includes nine dialects which cover nine distinct regions of British Isles. Acoustic properties such as spectral and prosodic features are derived from word level to construct the feature vector. Further, two different classification algorithms such as support vector machine (SVM) and tree-based extreme gradient boosting (XGB) ensemble algorithms are used to extract the prominent patterns that are used to discriminate the dialects. From the experiments, a better performance has been observed with word level traits using ensemble methods over the SVM classification method. © 2017 IEEE.
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    Robust Dialect Identification System using Spectro-Temporal Gabor Features
    (Institute of Electrical and Electronics Engineers Inc., 2018) Chittaragi, N.B.; Mothukuri, S.P.; Hegde, P.; Koolagudi, S.G.
    Automatic identification of dialects of a language is gaining popularity in the field of automatic speech recognition (ASR) systems. The present work proposes an automatic dialect identification (ADI) system using 2D Gabor and spectral features. A comprehensive study of the five dialects of a Dravidian Kannada language has been taken up. Gabor filters representing spectro-temporal modulations attempt in emulation of the human auditory system concerning signal processing strategies. Hence, they are able to well perceive human voices in tern recognize dialectal variations effectively. Also, spectral features Mel frequency cepstral coefficients (MFCC) are derived. A single classifier based support vector machine (SVM) and ensemble based extreme random forest (ERF) classification methods are employed for recognition. The effectiveness of the Gabor features for ADI system is demonstrated with proposed Kannada dialect dataset along with a standard intonation variation in English (IViE) dataset for British English dialects. The Gabor features have shown better performance over MFCC features with both datasets. Better recognition performance of 88.75% and 99.16% is achieved with Kannada and IViE dialect datasets respectively. Proposed Gabor features have demonstrated better performances even under noisy conditions. © 2018 IEEE.