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.Spectral features

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    Age approximation from speech using Gaussian mixture models
    (IEEE Computer Society help@computer.org, 2013) Mittal, T.; Barthwal, A.; Koolagudi, S.G.
    In this work, spectral features are extracted from speech to perform speaker classification based on thier age. Mel frequency cepstral coefficients (MFCCs) are explored as features. Gaussian mixture models (GMMs) are proposed as classifiers. The age groups considered in this study are 1-10, 11-20, 21-30, 31-40 and 41-50. The age-group database used in this work is recorded in Hindi from speakers of different ages and dialects containing five Hindi text prompts. The text prompts are constructed using textually neutral Hindi words recorded in neutral emotion which are used for characterizing the age group, for both male and female. Average age recognition performance, in the case of multiple speaker database is observed to be around 92.0%. © 2013 IEEE.
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    Sentence-Based Dialect Identification System Using Extreme Gradient Boosting Algorithm
    (Springer, 2020) Chittaragi, N.B.; Koolagudi, S.G.
    In this paper, a dialect identification system (DIS) is proposed by exploring the dialect specific prosodic features and cepstral coefficients from sentence-level utterances. Commonly, people belonging to a specific region follow a unique speaking style among them known as dialects. Sentence speech units are chosen for dialect identification since it is observed that a unique intonation and energy patterns are followed in sentences. Sentences are derived from a standard Intonational Variations in English (IViE) speech dataset. In this paper, pitch and energy contour are used to derive intonation and energy features respectively by using Legendre polynomial fit function along with five statistical features. Further, Mel frequency cepstral coefficients (MFCCs) are added to capture dialect specific spectral information. Extreme Gradient Boosting (XGB) ensemble method is employed for evaluation of the system under individual and combinations of features. Obtained results have indicated the influences of both prosodic and spectral features in recognition of dialects, also combined feature vectors have shown a better DIS performance of about 89.6%. © 2020, Springer Nature Singapore Pte Ltd.