ETRI 음성/언어정보연구센터에서는 분산음성인식을 위해 메모리를 작게 사용하며 속도가 빠른 음소인식기를 개발 중이다. 음향 모델, 언어 모델, 탐색 네트워크 등 고정되어 있는 정보는 인식기를 수행하기 이전에 미리 binary 형태로 구축하여 ROM 형태로 저장함으로써 실제 사용해야 할 RAM 용량을 대폭 줄일 수 있었다. Tied state에 기반한 triphone 모델에서는 unique HMM 만을 사용함으로써 인식시간 및 메모리 사용량을 대폭 줄일 수 있었다. Monophone 인식기의 경우 RAM 사용량이 179KB였으며, triphone 인식기의 경우 435KB의 RAM 사용량과 RTF(Real Time Factor) 0.02를 확인하였다.
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