Background to Pattern Matching and Alignment via DTW
Contents
2. Background to Pattern Matching and Alignment via DTW#
2.1. DTW in Speech Recognition#
What is Dynamic Time Warping?
“In time series analysis, dynamic time warping (DTW) is an algorithm for measuring similarity between two temporal sequences, which may vary in speed”
Dynamic Time Warping was first developed as a tool for speech pattern recognition and matching. It applies a particular dynamic programming algorithm that allows a quantification of similarities and alignment of different time- and/ or depth-series based on metrics of differences between these curves.
A typical mapping figure:
Fig. 2.1 Mapping path from Itakura, 1975 (their Fig. 1). Perhaps the resemblance to a depth-time or depth-depth geological mapping is clear.#
The first publications introducing this technique were:
2.1.1. Section Bibliography#
- 1
T. K. Vintsyuk. Speech discrimination by dynamic programming. Cybernetics, 4(1):52–57, 1968. URL: https://link.springer.com/content/pdf/10.1007/BF01074755.pdf, doi:10.1007/BF01074755.
- 2
F. Itakura. Minimum prediction residual principle applied to speech recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-23(1):67–72, 1975. URL: https://www.ee.columbia.edu/~dpwe/papers/Itak75-lpcasr.pdf, doi:10.1109/TASSP.1975.1162641.
- 3
H. Sakoe and S. Chiba. Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, ASSP-26(1):43–49, 1978. URL: https://www.irit.fr/~Julien.Pinquier/Docs/TP_MABS/res/dtw-sakoe-chiba78.pdf, doi:10.1109/TASSP.1978.1163055.