DYNAMIC-PROGRAMMING ALGORITHM OPTIMIZATION FOR SPOKEN WORD RECOGNITION
DYNAMIC-PROGRAMMING ALGORITHM OPTIMIZATION FOR SPOKEN WORD RECOGNITION
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DOI：
10.1109/tassp.1978.1163055
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发表时间：
1978-01-01
期刊：
影响因子：
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通讯作者：
CHIBA, S
中科院分区：
文献类型：
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作者：
SAKOE, H;CHIBA, S
本文提出了一种基于最优动态采样（DP）的语音识别时间归一化算法。首先，利用时间规整函数给出了时间归一化的一般原理。然后，两个时间归一化的距离定义，称为对称和非对称的形式，从原则。通过理论探讨和实验研究，对这两种形式进行了比较。证明了对称形式算法的优越性。成功地引入了一种新的技术，称为斜率约束，在该技术中，翘曲函数斜率的限制，以提高在不同类别的词之间的区分。定性分析了有效坡度约束特性，并通过实验确定了最佳坡度约束条件。优化的算法，然后广泛进行实验比较与各种DP算法，以前应用于口语单词识别由不同的研究小组。实验表明，本算法给出不超过约三分之二的错误，即使是最好的传统算法相比。
This paper reports on an optimum dynamic progxamming (DP) based time-normalization algorithm for spoken word recognition. First, a general principle of time-normalization is given using time-warping function. Then, two time-normalized distance definitions, called symmetric and asymmetric forms, are derived from the principle. These two forms are compared with each other through theoretical discussions and experimental studies. The symmetric form algorithm superiority is established. A new technique, called slope constraint, is successfully introduced, in which the warping function slope is restricted so as to improve discrimination between words in different categories. The effective slope constraint characteristic is qualitatively analyzed, and the optimum slope constraint condition is determined through experiments. The optimized algorithm is then extensively subjected to experimental comparison with various DP-algorithms, previously applied to spoken word recognition by different research groups. The experiment shows that the present algorithm gives no more than about two-thirds errors, even compared to the best conventional algorithm.