Implementation of Voice Recognition Via CNN and LSTM
Gyeongseop Shin1, Sang-Hong Lee2
1Gyeongseop Shin, Department of Computer Science & Engineering, Anyang University, Anyang-si, Republic of Korea.
2Sang-Hong Lee*, Department of Computer Science & Engineering, Anyang University, Anyang-si, Republic of Korea.
Manuscript received on January 16, 2020. | Revised Manuscript received on January 23, 2020. | Manuscript published on February 10, 2020. | PP: 1842-1844 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1832029420/2020©BEIESP | DOI: 10.35940/ijitee.D1832.029420
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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: The voice recognition system uses CNN a lot. This is because CNN has the optimized ability to recognize and classify targets. CNN, however, has a problem that the bigger the object to be recognized, the more expensive the computational costs are. In this paper, we are going to solve these problems through MFCC feature extraction and model roll combining CNN and LSTM to present the possibility of performing voice recognition even through low-cost devices.
Keywords: Voice Recognition, CNN, LSTM, MFCC.
Scope of the Article: Pattern Recognition