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Dynamic programming algorithm optimization for spoken word recognition (1978)
| Venue: | IEEE TRANSACTIONS ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING |
| Citations: | 786 - 3 self |
BibTeX
@ARTICLE{Sakoe78dynamicprogramming,
author = {Hiroaki Sakoe and Seibi Chiba},
title = {Dynamic programming algorithm optimization for spoken word recognition},
journal = {IEEE TRANSACTIONS ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING},
year = {1978},
volume = {26},
pages = {43--49}
}
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Abstract
This paper reports on an optimum dynamic programming (DP) based time-normalization algorithm for spoken word recognition. First, a general principle of time-normalization is given using timewarping function. Then, two time-normalized distance definitions, ded 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. AND SEIBI CHIBA vestigations were made, based on the assumption that speech patterns are time-sampled with a common and uniform sampling period, as in most general cases. One of the problems discussed in this paper involves the relative superiority of either a symmetric form of DP-matching or an asymmetric one. In the asymmetric form, time-normalization is achieved by trans-forming the time axis of a speech pattern onto that of the other. In the symmetric form, on the other hand, both time axes are transformed onto a temporarily defined common axis. Theoretical and experimental comparisons show that the sym-metric form gives better recognition than the asymmetric one. Another problem discussed concerns slope constraint technique. Since too much of the warping function flexibility sometimes results in poor discrimination between words in different 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 experimentat 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 twothirds errors, even compared to the best conventional algorithm. categories, a constraint is newly introduced on the warping I.
Keyphrases
spoken word recognition algorithm optimization asymmetric form speech pattern symmetric form function flexibility various dp-algorithms optimum dynamic programming warping function slope time ax optimum slope constraint condition relative superiority twothirds error experimental study abstract-this paper report experimental comparison different research group seibi chiba vestigations asymmetric one slope constraint constraint technique time-normalization algorithm theoretical discussion symmetric form algorithm superiority uniform sam-pling period time-normalized distance definition defined common axis effective slope constraint characteristic word recognition conventional algorithm poor discrimination sym-metric form time axis general case present algorithm general principle different category new technique optimized algorithm
