Work place: Department of Electronics and Communication Engineering, National Institute of Technology, Kurukshetra, India



Research Interests: Bioinformatics


Saloni, received his M.Tech in    Microelectronics and VLSI design from Kurukshetra University Kurukshetra. Currently, she is pursuing PhD at National Institute of technology in the department of Electronics and Communication. Her research interests include biomedical signal processing and VLSI design.

Author Articles
Estimation and Statistical Analysis of Physical Task Stress on Human Speech Signal

By Saloni R. K. Sharma Anil K. Gupta

DOI:, Pub. Date: 8 Oct. 2016

Human speech signal is an acoustic wave, which conveys the information about the words or message being spoken, identity of the speaker, language spoken, the presence and type of speech pathologies, the physical and emotional state of the speaker. Speech under physical task stress shows variations from the speech in neutral state and thus degrades the speech system performance. In this paper we have characterized the voice samples under physical stress and the acoustic parameters are compared with the neutral state voice parameters. The traditional voice measures, glottal flow parameters, mel frequency cepstrum coefficients and energy in various frequency bands are used for this characterization. T-test is performed to check the statistical significance of parameters. Significant variations are noticed in the parameters under two states. Pitch, intensity, energy values are high for the physically stressed voice; On the other hand glottal parameter values get decreased. Cepstrum coefficients shift up from the coefficients of neutral state voice samples. Energy in lower frequency bands was more sensitive to physical stress. This study improves the performance of various speech processing applications by analyzing the unwanted effect of physical stress in voice. 

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Voice Analysis for Telediagnosis of Parkinson Disease Using Artificial Neural Networks and Support Vector Machines

By Saloni R. K. Sharma Anil K. Gupta

DOI:, Pub. Date: 8 May 2015

Parkinson is a neurological disease and occurs due to lack of dopamine neurons. These dopamine neurons manage all body movements. Parkinson patients have difficulty in doing all daily routine activities, and also have disturbed vocal fold movements. Using voice analysis disease can be diagnosed remotely at an early stage with more reliability and in an economic way. In this paper, we have used 23 features dataset, all the features are analyzed and 15 features are selected from the total dataset. As in Parkinson tremor is present in the voice box muscles, so the variation in the period and amplitude of consecutive vocal cycles is present. The feature dataset selected consist of jitter, shimmer, harmonic to noise ratio, DFA, spread1 and PPE. Various classifiers are used and their comparison is done to find out which classifier is perfect in this environment. It is concluded that support vector classifiers as the best one with an accuracy of 96%. In the neural network classifiers with different transfer functions, there is tradeoff among the performance parameters.

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Classification of High Blood Pressure Persons Vs Normal Blood Pressure Persons Using Voice Analysis

By Saloni R. K. Sharma Anil K. Gupta

DOI:, Pub. Date: 8 Nov. 2013

The human voice is remarkable, complex and delicate. All parts of the body play some role in voice production and may be responsible for voice dysfunction. The larynx contains muscles that are surrounded by blood vessels connected to circulatory system. The pressure of blood in these vessels should be related with dynamic variation of vocal cord parameters. These parameters are directly related with acoustic properties of speech. Acoustic voice analysis can be used to characterize the pathological voices.  This paper presents the classification of high blood pressure and normal with the aid of voice signal recorded from the patients. Various features have been extracted from the voice signal of healthy persons and persons suffering from high blood pressure. Simulation results show differences in the parameter values of healthy and pathological persons. Then an optimum feature vector is prepared and kmean classification algorithm was implemented for data classification. The 79% classification efficiency was obtained.

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