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Voice Recognition-Based on Adaptive MFCC and Deep Learning for Embedded Systems

임베디드 시스템에서 사용 가능한 적응형 MFCC 와 Deep Learning 기반의 음성인식

  • Bae, Hyun Soo (Department of Electrical Engineering, Yeungnam University) ;
  • Lee, Ho Jin (Department of Electrical Engineering, Yeungnam University) ;
  • Lee, Suk Gyu (Department of Electrical Engineering, Yeungnam University)
  • Received : 2016.07.03
  • Accepted : 2016.08.30
  • Published : 2016.10.01

Abstract

This paper proposes a noble voice recognition method based on an adaptive MFCC and deep learning for embedded systems. To enhance the recognition ratio of the proposed voice recognizer, ambient noise mixed into the voice signal has to be eliminated. However, noise filtering processes, which may damage voice data, diminishes the recognition ratio. In this paper, a filter has been designed for the frequency range within a voice signal, and imposed weights are used to reduce data deterioration. In addition, a deep learning algorithm, which does not require a database in the recognition algorithm, has been adapted for embedded systems, which inherently require small amounts of memory. The experimental results suggest that the proposed deep learning algorithm and HMM voice recognizer, utilizing the proposed adaptive MFCC algorithm, perform better than conventional MFCC algorithms in its recognition ratio within a noisy environment.

Acknowledgement

Grant : BK21플러스

Supported by : 영남대학교

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