Studies on Paralinguistic Speech Sounds

Kadali, Devi Bhavani; Mittal, Vinay Kumar

doi:10.1109/indicon49873.2020.9342586

Cited by 4 publications

References 46 publications

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Enhancing Signal in Noisy Environment: A Review

Devi¹,

Mittal

2021

Smart Innovation, Systems and Technologies

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Noise is present in all environments. In the signal processing domain also, there is a presence of noise. In any application, after processing, we need a signal which is free from noise. So to obtain a high-quality signal, elimination and reduction of noise are important. The different noises include Salt-Pepper, Gaussian, Speckle, Poisson, etc. So in order to eliminate this noise, some of the noise reduction techniques or filters can be employed in between. This survey paper presents the elimination of noise in images during duct, rain, snow, etc., elimination of noise in speech signals or mobile audio in high noisy environment such as conferences, meetings, heavy traffic conditions, and elimination of noise in biomedical signals in the presence of other noisy signals. For this purpose, many filtering techniques can be used, and depending on the filtered image obtained, we can find out which is best suited for the specific purpose.

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Enhancing Signal in Noisy Environment: A Review

Devi¹,

Mittal

2021

Smart Innovation, Systems and Technologies

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Early Detection of Parkinson’s Disease Through Speech Features and Machine Learning: A Review

Gullapalli

Mittal

2021

Smart Innovation, Systems and Technologies

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Parkinson's Disease (PD) is a kind of neurodegenerative disorder. The disease causes communication impairment based on its progression. In general, identification of PD carried out based on medical images of brain. But it was recently identified that voice is acting as biomarkers for several neurological disorders. A review of speech features and machine learning algorithms is presented. This might be helpful for development of a non-invasive signal processing techniques for early detection of PD. Several models developed for disease detection is discussed, which are developed based on features like acoustic, phonation, articulation, dysphonia, etc. Machine learning algorithms like Logistic Regression (LG), Support Vector Machine (SVM), Boosting Regression Tree, bagging Regression, etc., and their performance accuracies in classification of Patient with PD (PWP) and Healthy Controls (HC) are reviewed. All these classification algorithms are trained and tested on several repository corpuses and customized datasets. The Spontaneous Speech (SS) is an efficient tool for the early detection of diseases like Parkinson's, Alzheimer's, Autism and several other dementia types in elderly people.

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Studies on biomedical signal processing: A review

Jalagam,

Mittal

2024

AIP Conference Proceedings

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Studies on Paralinguistic Speech Sounds

Cited by 4 publications

References 46 publications

Enhancing Signal in Noisy Environment: A Review

Enhancing Signal in Noisy Environment: A Review

Early Detection of Parkinson’s Disease Through Speech Features and Machine Learning: A Review

Studies on biomedical signal processing: A review

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