Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Gender Identification from Children's Speech(Institute of Electrical and Electronics Engineers Inc., 2018) Ramteke, P.B.; Dixit, A.A.; Supanekar, S.; Dharwadkar, N.V.; Koolagudi, S.G.Children's speech can be characterized by higher pitch and format frequencies compared to the adult speech. Gender identification task from children's speech is difficult as there is no significant difference in the acoustic properties of male and female child. Here, an attempt has been made to explore the features efficient in discriminating the gender from children's speech. Different combinations of spectral features such as Mel-frequency cepstral coefficients (MFCCs), ΔMFCCs and ΔΔMFCCs, Formants, Linear predictive cepstral coefficients (LPCCs); Shimmer and Jitter; Prosodic features like pitch and its statistical variations along with Δpitch related features are explored. Features are evaluated using non linear classifiers namely Artificial Neural Network (ANNs), Deep Neural Network (DNNs) and Random Forest (RF). From the results it is observed that the RF achieves an highest accuracy of 84.79% amongst the other classifiers. © 2018 IEEE.Item Gender Identification using Spectral Features and Glottal Closure Instants (GCIs)(Institute of Electrical and Electronics Engineers Inc., 2019) Ramteke, P.B.; Supanekar, S.; Koolagudi, S.G.Automatic identification of gender from speech may help to improve the performance of the systems such as speaker speech recognition, forensic analysis, authentication processes. The difference in the physiological parameters of male and female vocal folds results in significant changes in their vocal fold vibration pattern. These changes can be characterized from the differences in the duration of their glottal closure. In this paper, an attempt has been made for gender recognition from speech using spectral features such as MFCCs, LPCCs, etc.; pitch (F0), excitation source features like glottal closure instants (GCIs) and its statistical variations. Western Michigan University's Gender dataset is used for experimentation. The dataset is collected from 93 speakers consisting of speech from 45 male and 48 female speakers respectively. Random forests (RFs) and Support vector machines (SVMs) are used to measure the performance of the proposed features. Random forest is observed to achieve average frame level accuracy of 96.908% using 13 MFCCs, 13 LPCCs, Pitch (F0) and GCI Stats (5). SVM is observed to achieve an average accuracy of 98.607% using 13 MFCCs, 13 LPCCs and GCI Stats (5). From the results, it is observed that the proposed features are efficient in discriminating the gender from speech. © 2019 IEEE.Item Fake News Detection Using Machine Learning Algorithms(Association for Computing Machinery, 2022) Imbwaga, J.L.; Chittaragi, N.; Koolagudi, S.G.There has been an exponential growth in users sharing news and information in real-time on various social media platforms worldwide. However, few of the users share fake and misleading news for various reasons. The reasons for sharing fake news may not be limited to financial, personal, and/or political gain. Since users cannot determine or censor the type of content that appears on their respective platforms, fake news can pose significant and detrimental effects on an individual and society at large. In this regard, we have proposed the work with the primary objective of development of a fake news detection system by applying supervised machine learning algorithms on an annotated (labeled) dataset. The dataset was selected from Kaggle, consisting of fake news with 23503 entries and true news with 21418 entries. An overall better accuracies are observed with tree-based decision tree classifiers and a gradient boosting ensemble algorithm. © 2022 ACM.