Conference Papers

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

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    Energy sector reforms in India - a review
    (2008) Mangalpady, M.; Raj, M.G.
    Indian economy has been growing at a rate of 6-8 % annually during the 10th plan period (2002-07), which requires growth of basic infrastructural facilities at a still higher rate. Power sector is one of the major components of infrastructure development, which requires a growth rate of 9-10 % during the 11th and 12th plan periods (2007-2016). This requires huge amount of investments and restructuring of power sector, for which Government cannot fund the entire amount independently. Hence, private participation is necessary either as an independent venture or through public-private partnership (PPP). Electricity Act 2003 is a step in the direction of reforms by creating an environment for private participation in the generation, transmission and distribution of power in the country. The main aim of this Act is to implement proper steps for efficient and optimum use of energy resources available in India and to supply quality power at good reliability and optimum cost to the Indian consumers. However, one of the major constraints faced by the power sector is the lack of adequate R&D support. This paper attempts to throw a light on present status of Indian power sector with respect to generation, transmission and distribution of electricity.
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    Solar PV- diesel hybrid energy system for rural applications
    (2010) Mandi, R.P.; Yaragatti, U.R.
    This paper describe sizing of solar PV, DG set and battery bank for different configurations for share of solar power. Different configurations for integration of solar with diesel energy systems are explained in detail. The energy availability and reliability of the integrated energy system are highlighted The economics for implementation of different configurations are dealt with detailed discussion in this paper. ©2010 IEEE.
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    Multiclass SVM-based language-independent emotion recognition using selective speech features
    (Institute of Electrical and Electronics Engineers Inc., 2014) Kokane Amol, T.; Guddeti, G.R.M.
    In this paper, we emphasize on recognizing six basic emotions viz. Anger, Disgust, Fear, Happiness, Neutral and Sadness using selective features of speech signal of different languages like Germen and Telugu. The feature set includes thirteen Mel-Frequency Cepstral Coefficients (MFCC) and four other features of speech signal such as Energy, Short Term Energy, Spectral Roll-Off and Zero-Crossing Rate (ZCR). The Surrey Audio-Visual Expressed Emotion (SAVEE) Database is used to train the Multiclass Support Vector Machine (SVM) classifier and a German Corpus EMO-DB (Berlin Database of Emotional Speech) and Telugu Corpus IITKGP: SESC are used for emotion recognition. The results are analyzed for each speech emotion separately and obtained accuracies of 98.3071% and 95.8166 % for Emo-DB, IITKGP: SESC databases respectively. © 2014 IEEE.
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    Feature analysis for mispronounced phonemes in the case of alvoelar approximant (/r/) substituted with voiced dental consonant (/∂/)
    (Institute of Electrical and Electronics Engineers Inc., 2015) Ramteke, P.B.; Koolagudi, S.G.; Prabhakar, A.
    Mispronunciation is commonly observed in children from age 2 to 8 years. Some of the common mispronunciations are stopping, fronting, backing and affrication. These processes are known as phonological processes. Identification of these processes is crucial in studying the vocal tract development pattern and treating the phonological disorders in children. The features that clearly discriminate correctly pronounced phoneme from corresponding mispronounced phoneme have to be compared to identify the phonological processes. This paper focuses on the analysis of mispronounced alveolar approximant (/r/) substituted with voiced fricative consonant (/∂/). In this work, spectral and pitch related features are considered for the analysis using scatter plots and histograms. From the analysis, it is observed that the energy feature against 2nd and 4th cepstral coefficients achieves 75% and 65% discrimination respectively. © 2015 IEEE.
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    Recognition of repetition and prolongation in stuttered speech using ANN
    (Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2016) Savin, P.S.; Ramteke, P.B.; Koolagudi, S.G.
    This paper mainly focuses on repetition and prolongation detection in stuttered speech signal. The acoustic and pitch related features like Mel-frequency cepstral coefficients (MFCCs), formants, pitch, zero crossing rate (ZCR) and Energy are used to test the effectiveness in recognizing repetitions and prolongations in stammered speech. Artificial Neural Networks (ANN) are used as classifier. The results are evaluated using combination of different features. The results show that the ANN classifier trained using MFCC features achieves an average accuracy of 87.39% for repetition and prolongation recognition. © Springer India 2016.