Editing Dynamic Time Warping (section)

== Historical Context ==
The roots of Dynamic Time Warping (DTW) can be traced back to the 1960s when it was first introduced as an algorithm for solving time series alignment problems. In the 1970s, DTW began to gain prominence through its application to speech recognition tasks.<ref name=":0">Senin, Pavel. ‘Dynamic Time Warping Algorithm Review’, n.d.</ref>

During this early period of research, scientists were grappling with fundamental challenges in recognizing spoken words and phrases. Even when the same word was spoken by the same person multiple times, factors like speech rate, emphasis, and coarticulation effects resulted in nonlinear time distortions when comparing utterances. These variations made it difficult to precisely match spoken words against templates or reference patterns, as the alignment of speech signals was non-linear and complex. This misalignment of timing poses a significant problem for accurate distance measurement in speech recognition systems. Even small shifts can lead to incorrect identification of words. Researchers explored linear normalization techniques to account for timing differences, but these proved insufficient for complex speech patterns.<ref>Sakoe, H., and S. Chiba. ‘Dynamic Programming Algorithm Optimization for Spoken Word Recognition’. ''IEEE Transactions on Acoustics, Speech, and Signal Processing'' 26, no. 1 (February 1978): 43–49. <nowiki>https://doi.org/10.1109/TASSP.1978.1163055</nowiki>.</ref>

The groundbreaking contribution of DTW was its ability to address these challenges effectively. DTW allowed for "elastic" transformation of time series, which means it could align and compare speech signals with different time scales and phases. This flexibility in time-series alignment was crucial in mitigating the effects of shifting and distortion in speech signals.<ref name=":0" /> In parallel with these challenges, [[DARPA Speech Understanding Research|DARPA's Speech Understanding Research]] program in the 1970s aimed to enhance the capabilities of speech recognition systems, paving the way for innovative approaches like Dynamic Time Warping (DTW) to address the complexities of speech pattern alignment.

One of the primary motivations for developing DTW was to improve the accuracy of speech recognition systems. Traditional methods that relied on fixed templates for word recognition struggled to handle the variations in pronunciation time. DTW emerged as a method to measure the similarity of spoken patterns with different time zones, effectively solving the time alignment problem that had previously plagued speech recognition.

T. K. Vintsyuk's seminal paper titled "Speech Discrimination by Dynamic Programming" in 1972 played a significant role in introducing DTW as a powerful tool for speech recognition. This work emphasized the importance of time normalization, where the duration of unknown words is equated to that of standard words, to enhance recognition accuracy.<ref>Vintsyuk, T. K. ‘Speech Discrimination by Dynamic Programming’. ''Cybernetics'' 4, no. 1 (1972): 52–57. <nowiki>https://doi.org/10.1007/BF01074755</nowiki>.</ref>

In 1978, Sakoe and Chiba proposed using DTW for automatic word recognition. DTW allowed for an "elastic" time alignment through non-linear warping of the time axis between two time series. This dynamic programming approach was shown to outperform previous linear methods by more accurately modeling intrinsic fluctuations in speech. Around the same time, other pioneering studies also employed DTW for "time normalization" prior to classification.<ref>Sakoe, H., and S. Chiba. ‘Dynamic Programming Algorithm Optimization for Spoken Word Recognition’. ''IEEE Transactions on Acoustics, Speech, and Signal Processing'' 26, no. 1 (February 1978): 43–49. <nowiki>https://doi.org/10.1109/TASSP.1978.1163055</nowiki>.</ref>