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학술지 Statistical Model-Based Noise Reduction Approach for Car Interior Applications to Speech Recognition
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저자
이성주, 강병옥, 정호영, 이윤근, 김형순
발행일
201010
출처
ETRI Journal, v.32 no.5, pp.801-809
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.10.1510.0024
협약과제
10MS4200, 모바일 플랫폼 기반 대화모델 적용 자연어 음성 인터페이스 기술개발, 이윤근
초록
This paper presents a statistical model-based noise suppression approach for voice recognition in a car environment. In order to alleviate the spectral whitening and signal distortion problem in the traditional decisiondirected Wiener filter, we combine a decision-directed method with an original spectrum reconstruction method and develop a new two-stage noise reduction filter estimation scheme. When a tradeoff between the performance and computational efficiency under resource-constrained automotive devices is considered, ETSI standard advance distributed speech recognition font-end (ETSI-AFE) can be an effective solution, and ETSI-AFE is also based on the decision-directed Wiener filter. Thus, a series of voice recognition and computational complexity tests are conducted by comparing the proposed approach with ETSI-AFE. The experimental results show that the proposed approach is superior to the conventional method in terms of speech recognition accuracy, while the computational cost and frame latency are significantly reduced. © 2010 ETRI.
키워드
Clean spectrum reconstruction, ETSI standard aurora advanced front-end, Gaussian mixture model, Speech enhancement, Speech recognition, Two-stage mel-warped wiener filter
KSP 제안 키워드
Computational Efficiency, Computational complexity, Conventional methods, Distributed speech recognition(DSR), Front-End, Gaussian mixture Model(GMM), Noise reduction(NR), Reconstruction method, Resource-constrained, Speech recognition accuracy, Statistical Model