Using frame correlation algorithm in a duration distribution based hidden Markov model  

作　　者：王作英 崔小东 

机构地区：[1]Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

出　　处：《Science China(Technological Sciences)》2000年第6期606-612,共7页中国科学（技术科学英文版）

摘　　要：The assumption of frame independence is a widely known weakness of traditional hidden Markov model (HMM). In this paper, a frame correlation algorithm based on the duration distribution based hidden Markov model (DDBHMM) is proposed. In the algorithm, an AR model is used to depict the low pass effect of vocal tract from which stems the inertia leading to frame correlation. In the preliminary experiment of middle vocabulary speaker dependent isolated word recognition, our frame correlation algorithm outperforms the frame independent one. The average error reduction is about 20% .The assumption of frame independence is a widely known weakness of traditional hidden Markov model (HMM). In this paper, a frame correlation algorithm based on the duration distribution based hidden Markov model (DDBHMM) is proposed. In the algorithm, an AR model is used to depict the low pass effect of vocal tract from which stems the inertia leading to frame correlation. In the preliminary experiment of middle vocabulary speaker dependent isolated word recognition, our frame correlation algorithm outperforms the frame independent one. The average error reduction is about 20% .

关 键 词：FRAME CORRELATION ALGORITHM DURATION DISTRIBUTION MARKOV model 

分 类 号：O211.63[理学—概率论与数理统计]