Rabiner, L.; Rosenberg, A.; Levinson, S., "Considerations in dynamic time warping algorithms for discrete word recognition," Acoustics, Speech and Signal Processing, IEEE Transactions on , vol.26, no.6, pp. 575-582, Dec 1978 and trying to rephrase the reasoning applying it to the software metrics alignment.
- Most recently discussed issue: the assumption that the telemetry time series should be the same length. In my opinion this constraint is very strong and we should not consider this in the most cases. Whether or not waterfall or agile processes are considered there are certain activity patterns (phases) already identified. What is not canonized is the length of each of the phases and we should be considering an "open-ended" alignment algorithm to match those process features.
- The second issue discussed before is the allowable changes in the warping function. By adjusting this allowances we basically adjusting the sensitivity of the algorithm to recognize peaks of activities as the independent events. Question is what is the range of optimal allowances?
- The last issue I was thinking of is whether or not there are some hidden preferences in the choice of the "query" or "template" time series. So far I would say that there are no difference, but I would work on the experimental proof that the warping function is symmetric (or not?).
While researching, I discovered this book, which potentially is the greatest answers source for my questions. Readings in Speech Recognition By Alex Waibel, Kai-Fu Lee
I've seen the following figure in the article and in the book and it seems to be illustrating two first questions I am asking here: first two plots are illustrating the effect of allowances in the warping function, while the third plot (UELM) illustrates the "open-ended alignment" approach.
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