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Aurora DB를 이용한 잡음 음성 인식실험을 위한 Segmental K-means 훈련 방식의 기반인식기의 구현


An Implementation of the Baseline Recognizer Using the Segmental K-means Algorithm for the Noisy Speech Recognition Using the Aurora DBHee-Keun Kim, Young-Joo ChungRecently, many studies have been done for speech recognition in noisy environments. Particularly, the Aurora DB has been built as the common database for comparing the various feature extraction schemes. However, in general, the recognition models as well as the features have to be modified for effective noisy speech recognition. As the structure of the HTK is very complex, it is not easy to modify the recognition engine. In this paper, we implemented a baseline recognizer based on the segmental K-means algorithm whose performance is comparable to the HTK in spite of the simplicity in its implementation.