디지털콘텐츠학회 논문지 (Journal of Digital Contents Society)

  • 제18권1호
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  • Pages.93-99
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  • 2017
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  • 1598-2009(pISSN)
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  • 2287-738X(eISSN)
한국디지털콘텐츠학회 (Digital Contents Society)
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CTC를 이용한 LSTM RNN 기반 한국어 음성인식 시스템

LSTM RNN-based Korean Speech Recognition System Using CTC

  • 이동현 (서강대학교 컴퓨터공학과) ;
  • 임민규 (서강대학교 컴퓨터공학과) ;
  • 박호성 (서강대학교 컴퓨터공학과) ;
  • 김지환 (서강대학교 컴퓨터공학과)
  • Lee, Donghyun (Department of Computer Science and Engineering, Sogang University) ;
  • Lim, Minkyu (Department of Computer Science and Engineering, Sogang University) ;
  • Park, Hosung (Department of Computer Science and Engineering, Sogang University) ;
  • Kim, Ji-Hwan (Department of Computer Science and Engineering, Sogang University)
  • 투고 : 2017.01.19
  • 심사 : 2017.02.25
  • 발행 : 2017.02.28
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초록

Long Short Term Memory (LSTM) Recurrent Neural Network (RNN)를 이용한 hybrid 방법은 음성 인식률을 크게 향상시켰다. Hybrid 방법에 기반한 음향모델을 학습하기 위해서는 Gaussian Mixture Model (GMM)-Hidden Markov Model (HMM)로부터 forced align된 HMM state sequence가 필요하다. 그러나, GMM-HMM을 학습하기 위해서 많은 연산 시간이 요구되고 있다. 본 논문에서는 학습 속도를 향상하기 위해, LSTM RNN 기반 한국어 음성인식을 위한 end-to-end 방법을 제안한다. 이를 구현하기 위해, Connectionist Temporal Classification (CTC) 알고리즘을 제안한다. 제안하는 방법은 기존의 방법과 비슷한 인식률을 보였지만, 학습 속도는 1.27 배 더 빨라진 성능을 보였다.

A hybrid approach using Long Short Term Memory (LSTM) Recurrent Neural Network (RNN) has showed great improvement in speech recognition accuracy. For training acoustic model based on hybrid approach, it requires forced alignment of HMM state sequence from Gaussian Mixture Model (GMM)-Hidden Markov Model (HMM). However, high computation time for training GMM-HMM is required. This paper proposes an end-to-end approach for LSTM RNN-based Korean speech recognition to improve learning speed. A Connectionist Temporal Classification (CTC) algorithm is proposed to implement this approach. The proposed method showed almost equal performance in recognition rate, while the learning speed is 1.27 times faster.

키워드

참고문헌

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