Browsing by Author "Sobhana, N.V."

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An adoption model describing clinician s acceptance of automated diagnostic system for tuberculosis
(2016) Panicker, R.O.; Soman, B.; Gangadharan, K.V.; Sobhana, N.V.
Computerised medical diagnosing systems are very important to all healthcare professionals, especially clinicians who help in clinical decision-making in complex situations. The acceptance of automated or computerised medical diagnosing system for Tuberculosis (TB) among clinicians is very essential for its effective implementation and usage. This paper proposes a framework that aims to examine factors that influence clinician s acceptance and use of computerised TB detection system. An extended Unified Theory of Acceptance and Use of Technology (UTAUT) model is adopted in the healthcare context of a developing country for this purpose. The proposed framework is expected to help researchers and clinicians to assess the uptake of modern technology by health care professionals and the tool could be used in other healthcare contexts also. This paper also reviewed previous research adopting UTAUT model, for identifying the constructs promoting the adoption of technology acceptance in health care context. 2016, IUPESM and Springer-Verlag Berlin Heidelberg.
An adoption model describing clinician’s acceptance of automated diagnostic system for tuberculosis
(Springer Verlag service@springer.de, 2016) Panicker, R.O.; Soman, B.; Gangadharan, K.V.; Sobhana, N.V.
Computerised medical diagnosing systems are very important to all healthcare professionals, especially clinicians who help in clinical decision-making in complex situations. The acceptance of automated or computerised medical diagnosing system for Tuberculosis (TB) among clinicians is very essential for its effective implementation and usage. This paper proposes a framework that aims to examine factors that influence clinician’s acceptance and use of computerised TB detection system. An extended Unified Theory of Acceptance and Use of Technology (UTAUT) model is adopted in the healthcare context of a developing country for this purpose. The proposed framework is expected to help researchers and clinicians to assess the uptake of modern technology by health care professionals and the tool could be used in other healthcare contexts also. This paper also reviewed previous research adopting UTAUT model, for identifying the constructs promoting the adoption of technology acceptance in health care context. © 2016, IUPESM and Springer-Verlag Berlin Heidelberg.
Speaker Recognition in Emotional Environment using Excitation Features
(Institute of Electrical and Electronics Engineers Inc., 2020) Thomas, T.; Spoorthy; Sobhana, N.V.; Koolagudi, S.G.
Speaker Recognition is known as the task of recognizing the person speaking from his/her speech. Speaker recognition has many applications including transaction authentication, access control, voice dialing, web services, etc. Emotive speaker recognition is important because in real life, human beings extensively express emotions during conversations, and emotions alter the human voice. A text-independent speaker recognition system is proposed in the work. The system designed is for emotional environment. The proposed system in this work is trained using the speech samples recorded in neutral environment and the system evaluation is performed in an emotional environment. Here, excitation source features are used to represent speaker-specific details contained in speech signal. The excitation source signal is obtained after separating the segmental level features from the voice samples. The excitation source signal is almost considered as a noise so identifying a speaker in an emotive environment is a challenging task. Excitation features include Linear Prediction (LP) residual, Glottal Closure Instance (GCI), LP residual phase, residual cepstrum, Residual Mel-Frequency Cepstral Coefficient (R-MFCC), etc. A decrease in performance is observed when the system is trained with neutral speech samples and tested with emotional speech samples. Different emotions considered for emotional speaker identification are happy, sad, anger, fear, neutral, surprise, disgust, and sarcastic For the classification of speakers the algorithms used are Gaussian Mixture Model (GMM), Support Vector Machine (SVM), K-Nearest Neighbor(KNN), Random Forest and Naive Bayes. Â© 2020 IEEE.
Spectral Features for Emotional Speaker Recognition
(Institute of Electrical and Electronics Engineers Inc., 2020) Pasala, P.; Spoorthy, V.; Koolagudi, S.G.; Sobhana, N.V.
Speaker recognition in an emotive environment is a bit challenging task because of influence of emotions in a speech. Identifying the speaker from the speech can be done by analyzing the features of the speech signal. In normal conditions, identifying a speaker is not a tedious task. Whereas, identifying the speaker in an emotional environment such as happy, sad, anger, surprise, sarcastic, fear etc. is really challenging, since speech becomes altered under emotions and noise. The spectral features of speech signal include Mel Frequency Cepstral Co-efficients(MFCC), Shifted Delta Cepstral Coefficients (SDCC), spectral centroid, spectral roll off, spectral flatness, spectral contrast, spectral bandwidth, chroma-stft, zero crossing rate, root mean square energy, Linear Prediction Cepstral Coefficients (LPCC), spectral subband centroid, Teager energy based MFCC, line spectral frequencies, single frequency cepstral coefficients, formant frequencies, Power Normalized Cepstral Coefficients (PNCC), etc. The features that are extracted from the speech signal are classified using classifiers. Support Vector Machine(SVM), Gaussian Mixture Model, Gaussian Naive Bayes, K-Nearest Neighbour, Random Forest and a simple Neural Network using Keras is used for classification. The important application include security systems in which a person can be identified by biometrics that is voice of the person. The work aims to identify the speaker in an emotional environment using spectral features and classify using any of the classification techniques and to achieve a high speaker recognition rate. Feature combinations can also be used to improve accuracy. The proposed model performed better than most of the state-of-The-Art methods. Â© 2020 IEEE.