Voice Based Retrieval using Convolution Neural Network in Deep Learning
M.Mamatha1, T.Bhaskar Reddy2
1M.Mamatha, Research Area-Speech Processing Professor in SKU, Anatantapur, AP. Research Area- Computer Science.
2T. Bhaskar Reddy, Membership-ICITE,GATE Qualified with good score. Professor in SKU, Anatantapur, AP. Research Area- Computer Science.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 1829-1831 | Volume-8 Issue-12, October 2019. | Retrieval Number: L28571081219/2019©BEIESP | DOI: 10.35940/ijitee.L2857.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In this paper, we propose a Content Based Voice Retrieval(CBVR) is used to search a specific audio files from a large data base. Using Deep learning features are learned automatically in the training phase. Convolution Neural Network is used in this research for CBVR. On this paper, we recommend a novel technique to key word search(KWS) in low-resource languages, which presents an replacement method for retrieving the phrases of curiosity, in particular for the out of vocabulary (OOV) ones. Our procedure contains the approaches of question-by using-illustration retrieval tasks into KWS and conducts the hunt by the use of the subsequence dynamic time warping (sDTW) algorithm. For this, text queries are modeled as sequences of function vectors and used as templates within the search. A Convolution neural network-headquartered model is informed to gain knowledge of a frame-degree distance metric to be used in sDTW and the right question model frame representations for this realized distance. This new procedure can be used as a substitute to traditional LVCSR-situated KWS programs, or in combination with them, to attain the intention of filling the gap between OOV and in-vocabulary (IV) retrieval performances.
Keywords : keyword search, low resource languages, out of vocabulary (OOV) terms, query modeling, distance metric learning, subsequence dynamic time warping.
Scope of the Article: Machine learning