Faculty Publications
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Item 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.Item Contribution of Telugu vowels in identifying emotions(Institute of Electrical and Electronics Engineers Inc., 2015) Shashidhar Koolagudi, G.; Shivakranthi, B.; Sreenivasa Rao, K.S.; Ramteke, P.B.This work is mainly intended at identifying emotion contribution of different vowels in Telugu language. Instead of processing the entire speech signal we propose to focus only vowel parts of the utterance (/a/, /i/, /u/, /e/ and /o/). By analysing the vowels we can discriminate the emotions. In this work spectral and prosodic features are used for studying the effect of emotions on different vowels. Even though prosodic features are best discriminators of emotions at utterance level, at phoneme level spectral features are more useful. One may observe that same vowel exhibits different spectral behaviour when expressed in different emotions. Shimmer and jitter play a crucial role for classifying emotions using vowels. A semi natural database used in this work is collected from Telugu movies. Gaussian Mixture Models (GMMs) are used as the mathematical models for classification. Emotions considered for this work are anger, fear, happy, sad and neutral. Average emotion recognition performance obtained by combining MFCCs, formants, intensity, shimmer and jitter is around 78%. © 2015 IEEE.Item Audio Replay Attack Detection for Speaker Verification System Using Convolutional Neural Networks(Springer, 2019) Kemanth, P.J.; Supanekar, S.; Koolagudi, G.K.An audio replay attack is one of the most popular spoofing attacks on speaker verification systems because it is very economical and does not require much knowledge of signal processing. In this paper, we investigate the significance of non-voiced audio segments and deep learning models like Convolutional Neural Networks (CNN) for audio replay attack detection. The non-voiced segments of the audio can be used to detect reverberation and channel noise. FFT spectrograms are generated and given as input to CNN to classify the audio as genuine or replay. The advantage of the proposed approach is, because of the removal of the voiced speech, the feature vector size is reduced without compromising the necessary features. This leads to significant amount of reduction on training time of the networks. The ASVspoof 2017 dataset is used to train and evaluate the model. The Equal Error Rate (EER) is computed and used as a metric to evaluate model performance. The proposed system has achieved an EER of 5.62% on the development dataset and 12.47% on the evaluation dataset. © 2019, Springer Nature Switzerland AG.Item Is the effect of Indian energy price shocks asymmetric on the stock market at the firm level? A panel SVAR approach(FrancoAngeli, 2021) Aruna, B.; Rajesh Acharya, R.H.This paper examines, using monthly data from 1995 to 2016, whether the oil, coal and electricity price shocks have an asymmetric influence on stock returns and inflation. The paper has employed Panel Structural Vector Autoregressive (PSVAR) model with various measures of the oil, coal and electricity price shocks on a dataset containing 1168 firms. Results from Panel-SVAR reveal that all oil, coal and electricity price specifications have an asymmetric impact on stock returns. Further, impulse response function reveals that the various dimensions of oil, coal and electricity price shocks lead to volatility in the response variables. It can also be observed that negative coal and electricity price shock has a radical impact on stock returns. Overall, the study on asymmetric impact of net oil and coal price increase, deserves attention from the investors and policy makers. © 2020 Franco Angeli Edizioni. All rights reserved.Item Does GRI compliance moderate the impact of sustainability disclosure on firm value?(Emerald Publishing, 2023) Sreepriya, J.; Suprabha, K.R.; Prasad, K.Purpose: This paper aims to examine the moderating role of global reporting initiative (GRI) compliance in the association between sustainability reporting and firm value. Design/methodology/approach: This study investigates a sample of 223 manufacturing firms, encompassing 11 industries from 2010 to 2019. Using GRI compliance as a moderator, the authors employed a generalized method of moments model to study how sustainability disclosure impacts firm value. Findings: The results indicate a positive and significant association between sustainability disclosure and firm value. This study reveals that GRI compliance moderates the relationship between sustainability disclosure and firm value, such that firm value increases when the firm adopts GRI in sustainability reporting. Originality/value: No prior studies have examined GRI compliance's direct and moderating effects on the association between sustainability disclosures and firm value in the Indian manufacturing sector. This study is also valuable for the managers and industry to understand the significance of implementing voluntary sustainability disclosure practices and being GRI compliant. © 2022, Emerald Publishing Limited.Item The impact of ESG disclosure on mitigating financial distress: exploring the moderating role of firm life cycle(Palgrave Macmillan, 2025) Suprabha, K.R.; Sreepriya, J.; Prasad, K.Globally, businesses are increasingly gaining recognition for their non-financial performance due to heightened stakeholder demands about ethical and environmental responsibilities. This noteworthy shift in perspective has catalyzed the inception of this research, which seeks to scrutinize the influence of environmental, social, and governance disclosure (ESGD) on financial distress. To investigate the potential capacity of ESGD in mitigating financial distress, the researchers have employed the Generalized Method of Moments. Additionally, the study takes into account the moderating role played by the firm’s life cycle in this relationship. The findings underscore that the adoption of ESGD is associated with a decreased likelihood of default, thus highlighting its effectiveness as a risk management strategy. Moreover, this investigation emphasizes the impact of the firm’s life cycle on the link between ESGD and corporate financial distress. Rooted in signalling theory as the theoretical framework, the research posits that a wide spectrum of Environmental, Social, and Governance (ESG) initiatives not only enhances the consistency of signals but also amplifies the associated signal costs. Consequently, in alignment with this theoretical perspective and substantiated by empirical evidence, our study confirms a multifaceted influence of ESGD on financial distress, contingent upon the distinctive phases of a firm's life cycle. In consequence, this study offers valuable insights for managerial decision-making, guiding the development of tailored disclosure policies that align with the specific characteristics of a firm’s life cycle. © The Author(s), under exclusive licence to Springer Nature Limited 2024.