본 논문에서는 HMM 기반의 연속음성인식에서 음향모델의 최적화 기법을 논한다. 대부분의 음성인식 시스템에서 HMM 상태별로동일한개수의가우시안성분(mixture component)을사용해왔다.그러나,음향모델링에사용되는데이터샘플의개수는 HMM 상태별로 다르므로 이에 따른 최적화를 수행할 경우 모델 파라미터의 개수를 효과적으로 줄일 수 있을 뿐아니라, 디코딩 단계에서 음성인식기의 속도 및 인식 성능 개선이 기대된다. 본 연구에서 제안한 방법은 기존에 알려진MDL (minimum description length) 기반의 음향모델 최적화 방법에서 가우시안 성분들의 통합과정에 가우시안 성분의가중치정보(mixture weight)를반영하도록개선하였다. 인식실험결과, 제안한방법은가우시안성분의가중치를반영하지 않는 기존 방법에 비해 향상된 최적화 성능을 보임을 확인할 수 있었다.
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