随着计算机技术和信息化的发展,人机交互在办公以及生活中显得越来越重要。由于手势具有灵活、直观、简单等优点,成为人机交互研究的重要领域。针对手势识别技术在自然人机交互中对时间和准确度要求较高的问题,提出一种新的手势识别算法...随着计算机技术和信息化的发展,人机交互在办公以及生活中显得越来越重要。由于手势具有灵活、直观、简单等优点,成为人机交互研究的重要领域。针对手势识别技术在自然人机交互中对时间和准确度要求较高的问题,提出一种新的手势识别算法(IDTW-K)。该算法对经典动态时间规整(Dynamic Time Warping,DTW)算法进行了改进。利用节点在运动序列中的距离方差对各个节点进行权值动态分配,并对DTW的搜索路径进行了详细的分析,采用点和线相结合的范围约束防止其搜索不合理以及优化DTW算法的计算速度,并结合KNN算法提高了手势识别效率。通过实验对IDTWK算法、改进的DTW算法和传统的DTW算法进行了对比,结果表明所提出的算法在精准度和识别速率上有一定的提高。展开更多
Obtaining training material for rarely used English words and common given names from countries where English is not spoken is difficult due to excessive time, storage and cost factors. By considering personal privacy...Obtaining training material for rarely used English words and common given names from countries where English is not spoken is difficult due to excessive time, storage and cost factors. By considering personal privacy, language- independent （LI） with lightweight speaker-dependent （SD） automatic speech recognition （ASR） is a convenient option to solve tile problem. The dynamic time warping （DTW） algorithm is the state-of-the-art algorithm for small-footprint SD ASR for real-time applications with limited storage and small vocabularies. These applications include voice dialing on mobile devices, menu-driven recognition, and voice control on vehicles and robotics. However, traditional DTW has several lhnitations, such as high computational complexity, constraint induced coarse approximation, and inaccuracy problems. In this paper, we introduce the merge-weighted dynamic time warping （MWDTW） algorithm. This method defines a template confidence index for measuring the similarity between merged training data and testing data, while following the core DTW process. MWDTW is simple, efficient, and easy to implement. With extensive experiments on three representative SD speech recognition datasets, we demonstrate that our method outperforms DTW, DTW on merged speech data, the hidden Markov model （HMM） significantly, and is also six times faster than DTW overall.展开更多
文摘随着计算机技术和信息化的发展,人机交互在办公以及生活中显得越来越重要。由于手势具有灵活、直观、简单等优点,成为人机交互研究的重要领域。针对手势识别技术在自然人机交互中对时间和准确度要求较高的问题,提出一种新的手势识别算法(IDTW-K)。该算法对经典动态时间规整(Dynamic Time Warping,DTW)算法进行了改进。利用节点在运动序列中的距离方差对各个节点进行权值动态分配,并对DTW的搜索路径进行了详细的分析,采用点和线相结合的范围约束防止其搜索不合理以及优化DTW算法的计算速度,并结合KNN算法提高了手势识别效率。通过实验对IDTWK算法、改进的DTW算法和传统的DTW算法进行了对比,结果表明所提出的算法在精准度和识别速率上有一定的提高。
基金supported by the Research Plan Project of National University of Defense Technology under Grant No.JC13-06-01the OCRit Project made possible by the Global Leadership Round in Genomics&Life Sciences Grant(GL2)
文摘Obtaining training material for rarely used English words and common given names from countries where English is not spoken is difficult due to excessive time, storage and cost factors. By considering personal privacy, language- independent （LI） with lightweight speaker-dependent （SD） automatic speech recognition （ASR） is a convenient option to solve tile problem. The dynamic time warping （DTW） algorithm is the state-of-the-art algorithm for small-footprint SD ASR for real-time applications with limited storage and small vocabularies. These applications include voice dialing on mobile devices, menu-driven recognition, and voice control on vehicles and robotics. However, traditional DTW has several lhnitations, such as high computational complexity, constraint induced coarse approximation, and inaccuracy problems. In this paper, we introduce the merge-weighted dynamic time warping （MWDTW） algorithm. This method defines a template confidence index for measuring the similarity between merged training data and testing data, while following the core DTW process. MWDTW is simple, efficient, and easy to implement. With extensive experiments on three representative SD speech recognition datasets, we demonstrate that our method outperforms DTW, DTW on merged speech data, the hidden Markov model （HMM） significantly, and is also six times faster than DTW overall.
