摘要
与单词相比,具有大量特征数据和复杂发音变化的英语语音在隐马尔可夫模型(HMM)中存在更多问题,例如维特比算法的复杂度计算和高斯混合模型中的概率分布问题;为了实现基于HMM和聚类的独立于说话人的英语语音识别系统,提出了用于降低语音特征参数维数的分段均值算法、聚类交叉分组算法和HMM分组算法的组合形式;实验结果表明,与单个HMM模型相比,该算法不仅提高了英语语音的识别率近3%,而且提高系统的识别速度20.1%。
For English sounds with a large number of characteristic data and complex pronunciation variations,there are more problems in hidden Markov models(HMM)than words,such as complexity calculation of Wittby algorithm and probability distribution in Gaussian mixture model.In order to realize the speech-independent English speech recognition system based on HMM and clustering,a combination of segmented mean algorithm,clustering crossover grouping algorithm and HMM grouping algorithm is proposed to reduce the dimension of speech feature parameters.Experimental results show that compared with the single HMM model,the algorithm not only improves the recognition rate of English speech by 3%,but also improves the recognition speed of the system by 20.1%.
作者
朱祥
Zhu Xiang(Arts&Sciences Branch College,Yangling Vocational&Technical College,Yangling712100,China)
出处
《计算机测量与控制》
2020年第5期175-179,共5页
Computer Measurement &Control
基金
杨凌职业技术学院人文社科研究基金项目(GJ19100)。
关键词
英语语音识别
隐马尔科夫模型
聚类
特征数据
English sentence recognition
hidden Markov model
clustering
feature data
作者简介
朱祥(1982-),男,陕西凤翔人,讲师,硕士,主要从事英语语言与教学方向的研究。
