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A Robust Feature Extraction with Dual Fusion aided Extreme Learning for Audio–Visual Hindi Speech Recognition

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Title Statement A Robust Feature Extraction with Dual Fusion aided Extreme Learning for Audio–Visual Hindi Speech Recognition
 
Added Entry - Uncontrolled Name Sharma, Usha ; Indian Institute of Technology (ISM) Dhanbad, India
Om, Hari ; CSE Department, Indian Institute of Technology (ISM) Dhanbad, India
Mishra, A N; Krishna Engineering College, Ghaziabad, India
 
Uncontrolled Index Term Speech recognition; Audio-visual; Jaya optimization; Bottleneck DNN; ELM
 
Summary, etc. <p>In Automatic Speech Recognition (ASR) based system implementation, robustness to several noisy background situation is a unique challenge. In this paper, for estimating both audio and visual aspect feature in light of different information representation perspectives directs to the robust feature extraction from audio-visual speech image. Further, the authors obtain the bottleneck features from the bottleneck layer of the bottleneck deep neural network (BN-DNN). Further, a familiar powerful texture descriptor of Local Binary Pattern (LBP) and Local Phase Quantization (LPQ) is applied to obtain the visual related features from the face region. Moreover, the categorization is executed utilizing the help of Extreme Learning Machine (ELM) and to reach the global optimum through Jaya optimization algorithm for audio-visual Hindi speech recognition. The proposed scheme is evaluated in MATLAB platform and the implementation is equated with the existing audio-visual speech recognition (AVSR) approaches.</p>
 
Publication, Distribution, Etc. Journal of Scientific and Industrial Research (JSIR)
2020-07-29 12:49:53
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/JSIR/article/view/38359
 
Data Source Entry Journal of Scientific and Industrial Research (JSIR); ##issue.vol## 79, ##issue.no## 05
 
Language Note en