Volume 11 - Volume 11
Enhanced Optimization in DCNN for Conversion of Non Audible Murmur to Normal Speech based on Dirichlet Process Mixture Feature
Abstract
In various applications towards mobile communication, the recognition of speech automatically plays
a vital role. The user communication devices for interaction require a large amount of vocabulary
recognition system, more preciseness and real time less power consuming schemas. The miniaturized
battery controlled devices suffer with power consumption and large memory bandwidth. Also the
speech challenge people's mobile devices require more attention. Therefore, a useful technology is
proposed to convert non-audible murmur to normal speech, based on Stochastic Biogeography-based
WOA (SBWOA) integrated with Dirichlet process mixture. The features like spectral skewness,
Taylor AMS, spectral centroid, pitch chroma and newly developed Dirichlet Process mixture features are extracted from the input murmured speech signal and trained in the DCNN. The identification of
speech is based on Deep Convolutional Neural Network (Deep CNN), which is trained by the
proposed Stochastic Biogeography WOA (SBWOA). The stochastic gradient descent method,
Biogeography-based optimization (BBO) and Whale optimization algorithm (WOA) are combined
together to improve the results in metric analysis. The TRP, FPR and Accuracy shows the improved
results of 0.9, 0.001 and 0.99 respectively.
Paper Details
PaperID: 2239
Author's Name: T. Rajesh Kumar, Arumbaka Srinivasa Rao, K. Kalaiselvi, S.S. Manivannan, C. Shahul Hameed and C.M. Velu
Volume: Volume 11
Issues: Volume 11
Keywords: Biogeography-based Optimization, Deep Convolutional Neural Network, Dirichlet Process Mixture, Speech Recognition, Stochastic Gradient Descent Approach.
Year: 2021
Month: June
Pages: 1818-1842