Dynamic Time warping for voice recognition

1. What is DTW in speech recognition?
2. Why is dynamic time warping useful?
3. How does DTW algorithm work?

What is DTW in speech recognition?

DTW is a method to measure the similarity of a pattern with different time zones. The smaller the distance produced, the more similar between the two sound patterns. Both sound patterns are similar, thus the two voices are said to be the same.

Why is dynamic time warping useful?

Dynamic Time Warping is used to compare the similarity or calculate the distance between two arrays or time series with different length. How to do that? One obvious way is to match up a and b in 1-to-1 fashion and sum up the total distance of each component.

How does DTW algorithm work?

Dynamic time warping (DTW) is a time series alignment algorithm developed originally for speech recognition⁽¹⁾. It aims at aligning two sequences of feature vectors by warping the time axis iteratively until an optimal match (according to a suitable metrics) between the two sequences is found.