Low-Resource Constraints for Speech Recognition using HDNN
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2018 by IJCTT Journal|
|Year of Publication : 2018|
|Authors : S. Kousar Bhanu|
|DOI : 10.14445/22312803/IJCTT-V61P113|
MLA Style: S. Kousar Bhanu "Low-Resource Constraints for Speech Recognition using HDNN" International Journal of Computer Trends and Technology 61.2 (2018):70-73.
APA Style:S. Kousar Bhanu (2018). Low-Resource Constraints for Speech Recognition using HDNN. International Journal of Computer Trends and Technology, 61(2),70-73
In Speech Recognition acoustic model is a document to represent the communication between audio signals that make up speech. In many approaches Neural Network develops an attractive acoustic modelling like Speaker Adaptation whereby adapted to acoustic features. The study determines that in Speech Recognition the Highway Deep Neural Network (HDNN’s) contains the two gate units that secured over all the hidden layers to supervise the way of the highway networks. These gate units are shared beyond all the hidden layers to reduce the size of model parameters, all the model parameters are updated in sequence training to improve the results. In this paper, HDNN is used for implementation of Gate functions using Stacked Autoencoder (SAE), a layer-wise approach to train Deep Neural Network based on Speech Repository analysis. These Encoders decides a Machine Learning Model to locate a Low level Dimensional portrayal of model parameters has taken from Direct Voice Input (DVI). DVI intended to voice command-and-control to read the parameters from the speech utterances for each speaker.
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Stacked Auto-Encoders, Automatic Speech Recognition, Stacked Auto-Encoders, Speech Recognition, HDNN, Acoustic Models