Conference Papers

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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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    Detection and Visualization of Corroded Surfaces Using Machine Learning
    (Springer Science and Business Media Deutschland GmbH, 2024) Shrivathsa, B.J.; Dhanya, R.; Meghana Nayak, D.; Pavan, G.S.
    The use of artificial intelligence in asset management greatly assists the industry and structural health monitoring systems. Using machine learning techniques for asset inspections can increase safety, reduce access costs, provide objective classification, and improve digital asset management systems. The detection and visualization of corrosion from digital images present significant advantages like automation, access to remote locations, mitigation of risk of inspectors, cost savings, and detecting speed. This paper used deep learning convolutional neural networks to build simple corrosion detection models and used an extreme gradient boosting algorithm to visualize the corroded surfaces. A large dataset of 1900 images with corrosion and without corrosion was collected using web scraping techniques and labeled accordingly. Training a deep learning model requires massive and high-resolution image datasets and intensive image labeling to approach high-level accuracy. The results and findings will improve the development of deep learning models for detecting and visualizing specific features on corroded surfaces. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.