A Case Study on Back-End Voice Activity Detection for Distributed Specch Recognition System Using Support Vector Machines

2014 ◽  
Author(s):  
Azzedine Touazi ◽  
Mohamed Debyeche
Keyword(s):  
Support Vector Machines ◽  
Recognition System ◽  
Voice Activity Detection ◽  
Support Vector ◽  
Activity Detection ◽  
Vector Machines ◽  
Voice Activity

Voice activity detection using optimal window overlapping especially over health-care infrastructure

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shilpa Sharma ◽  
Punam Rattan ◽  
Anurag Sharma ◽  
Mohammad Shabaz
Keyword(s):  
Health Care ◽  
Health Care Professionals ◽  
Research Work ◽  
Voice Activity Detection ◽  
Support Vector ◽  
Activity Detection ◽  
Content Type ◽  
Health Care Infrastructure ◽  
Voice Activity ◽  
Optimal Window

Purpose This paper aims to introduce recently an unregulated unsupervised algorithm focused on voice activity detection by data clustering maximum margin, i.e. support vector machine. The algorithm for clustering K-mean used to solve speech behaviour detection issues was later applied, the application, therefore, did not permit the identification of voice detection. This is critical in demands for speech recognition. Design/methodology/approach Here, the authors find a voice activity detection detector based on a report provided by a K-mean algorithm that permits sliding window detection of voice and noise. However, first, it needs an initial detection pause. The machine initialized by the algorithm will work on health-care infrastructure and provides a platform for health-care professionals to detect the clear voice of patients. Findings Timely usage discussion on many histories of NOISEX-92 var reveals the average non-speech and the average signal-to-noise ratios hit concentrations which are higher than modern voice activity detection. Originality/value Research work is original.

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