Editing Dynamic Time Warping (section)

== Impact ==
In Speech recognition, as the speech is a time-dependent process, the utterances of the same word will have different durations, and utterances of the same word with the same duration will differ in the middle, due to different parts of the words being spoken at different rates.<ref name=":1">T. B. Amin and I. Mahmood, "Speech Recognition using Dynamic Time Warping," 2008 2nd International Conference on Advances in Space Technologies, Islamabad, Pakistan, 2008, pp. 74-79, doi: 10.1109/ICAST.2008.4747690.</ref>In other words, the voice signal exhibits a high degree of variability, even for the same person pronounces the same word in the same way but in different time, they can’t be exactly the same because the variation arises from the unpredictable duration of each pronunciation. So it is not ideal to compare each word's feature vector sequence's effect directly.

Dynamic Time Warping algorithm is a prevailing approach which is practical and theoretical. Dynamic Time Warping algorithm is based on dynamic programming, problem solving approach and used for measuring similarity between two sequences, which may vary in time or space. Similarity is measured by computing a distance between two times series. <ref>Yadav M, Alam M A. Dynamic time warping (dtw) algorithm in speech: a review[J]. International Journal of Research in Electronics and Computer Engineering, 2018, 6(1): 524-528.</ref>It is an early and classical algorithm for speech recognition especially for isolated word recognition.

'''Improving Accuracy and Reducing Processing Time''' 

The impact of Dynamic Time Warping on speech recognition is significant. One of the problems of speech recognition is different timing of speech alignment, and two words from the same word by the same user can have different times, which means small changes may result in incorrect recognition. Dynamic Time Warping is an efficient method for solving time alignment problem. Dynamic Time Warping solves this problem by aligning words correctly and calculating the minimum distance between two words. <ref name=":1" />Dynamic Time Warping can utilize automated alignment methods, thereby eliminating the need for time consuming manual alignment procedures.<ref name=":2">Turk O, Arslan L M. Robust processing techniques for voice conversion[J]. Computer Speech & Language, 2006, 20(4): 441-467.</ref> It enhances the effectiveness and efficiency of speech recognition, leading to improved accuracy and reduced processing time.

'''Reduce the Impact of Background Noise'''

In real-world scenarios, background noise is often present, making speech recognition challenging. Dynamic Time Warping could be used to reduce the impact of background noise on speech recognition. Most of these DTW-related studies have either developed improved template-matching algorithms, or provided modified schemes for a DTW operation optimization framework for increasing the robustness of the recognition system.<ref>Ding Jr I, Yen C T, Hsu Y M. Developments of machine learning schemes for dynamic time-wrapping-based speech recognition[J]. Mathematical Problems in Engineering, 2013, 2013.</ref> Dynamic Time Warping could help distinguish between the speech signal and noise, improving recognition accuracy in noisy environment.

'''Reduce the Impact of Pronunciation Variations'''

Dynamic programming is a popular method employed to avoid discontinuities and hence improve the robustness of the pitch detection algorithm.<ref name=":2" /> It is well-known that individuals may pronounce words differently while maintaining the same meaning due to speakers' accent, dialect, gender or voice quality. It is important for speech recognition system to recognize speech from diverse sources. DTW's dynamic time alignment capability allowed it to better accommodate these variations.

'''Versatility Across Domains'''

Dynamic Time Warping is a versatile technique primarily used in time series analysis and pattern, and it have been used in many applications. For example, road surface monitoring can employ image processing and ultrasonic sensors based on Dynamic Time Warping.<ref>Sharma S K, Phan H, Lee J. An application study on road surface monitoring using DTW based image processing and ultrasonic sensors[J]. Applied Sciences, 2020, 10(13): 4490.</ref>Dynamic Time Warping is used to gesture recognition<ref>Hartmann B, Link N. Gesture recognition with inertial sensors and optimized DTW prototypes[C]//2010 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2010: 2102-2109.</ref>, which is useful in applications like sign language recognition or gesture-based control systems. Also, Dynamic Time Warping can be used in medical like X-ray diagnosis<ref>Adwan S, Alsaleh I, Majed R. A new approach for image stitching technique using Dynamic Time Warping (DTW) algorithm towards scoliosis X-ray diagnosis[J]. Measurement, 2016, 84: 32-46.</ref>and smart healthcare system<ref>Ismail A, Abdlerazek S, El-Henawy I M. Development of smart healthcare system based on speech recognition using support vector machine and dynamic time warping[J]. Sustainability, 2020, 12(6): 2403.</ref>.

'''Historical Significance'''

Traditional Dynamic Time Warping was one of the pioneering algorithms in speech recognition which offers important lessons for later developments and new recognition algorithms, like the [[Hidden Markov Models]]. The Hidden Markov Model and Dynamic Time Warping are actually sharing the same idea of DP(dynamic programming). <ref>Fang C. From dynamic time warping (DTW) to hidden markov model (HMM)[J]. University of Cincinnati, 2009, 3: 19.</ref>  Although HMMs and DTW have different roles in speech recognition systems, they are often used together to achieve better performance in speech recognition. Lessons learned from DTW's approach to dynamic programming have paved the way for more sophisticated algorithms and machine learning methods in speech recognition.