Voice Recognition System Through Machine Learning
Sindhu B1, B Sujatha2
1Sindhu. B, Department of Computer Science and Engineering, Godavari Institute of Engineering and Technology, Rajamahendravaram, Andhra Pradesh, India
2Dr B Sujatha, Department of Computer Science and Engineering, Godavari Institute of Engineering and Technology, Rajamahendravaram, Andhra Pradesh, India
Manuscript received on 16 August 2019 | Revised Manuscript received on 20 August 2019 | Manuscript published on 30 August 2019 | PP: 4478-4483 | Volume-8 Issue-10, August 2019 | Retrieval Number: J10720881019/19©BEIESP | DOI: 10.35940/ijitee.J1072.0881019
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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: Human voice recognition by computers has been ever developing area since 1952. It is challenging task for a computer to understand and act according to human voice rather than to commands or programs. The reason is that no two human’s voice or style or pitch will be similar, and every word is not pronounced by everyone in a similar fashion. Background noises and disturbances may confuse the system. The voice or accent of the same person may change according to the user’s mood, situation, time etc. despite of all these challenges, voice recognition and speech to text conversion has reached a successful stage. Voice processing technology deserves still more research. As a tip of iceberg of this research we contribute our work on this are and we propose a new method i.e., VRSML (Voice Recognition System through Machine Learning) mainly focuses on Speech to text conversion, then analyzing the text extracted from speech in the form of tokens through Machine Learning. After analyzing the derived text, reports are created in textual as well graphical format to represent the vocabulary levels used in that speech. As Supervised learning algorithm from Machine Learning is employed to classify the tokens derived from text, the reports will be more accurate and will be generated faster.
Keywords: Machine Learning, Speech Recognition, Speech to text conversion, Text analysis, Vocabulary
Scope of the Article: Machine/ Deep Learning with IoT & IoE