Dynamic time warping

About: Dynamic time warping is a research topic. Over the lifetime, 6013 publications have been published within this topic receiving 133130 citations.

A comparative analysis of regional correlation, dynamic time warping, and skeletal tree matching for signature verification

Abstract: A report is presented on a comparative study of three different signal matching algorithms in the context of signature verification: regional correlation, dynamic time warping, and skeletal tree matching. The algorithm performances are compared in a single experimental protocol over the same database. Algorithm performance is analyzed in terms of verification error rates, execution time, and number and sensitivity of algorithm parameters. Three different script types (normal signatures, handwritten passwords, and initials) and three different signal representation spaces (position, velocity, and acceleration) are considered. Verification errors show that no algorithm consistently outperforms the others in all circumstances. >

Featureweighting in dynamic timewarping for gesture recognition in depth data

Abstract: We present a gesture recognition approach for depth video data based on a novel Feature Weighting approach within the Dynamic Time Warping framework. Depth features from human joints are compared through video sequences using Dynamic Time Warping, and weights are assigned to features based on inter-intra class gesture variability. Feature Weighting in Dynamic Time Warping is then applied for recognizing begin-end of gestures in data sequences. The obtained results recognizing several gestures in depth data show high performance compared with classical Dynamic Time Warping approach.

Discriminative training of dynamic programming based speech recognizers

Abstract: A new minimum recognition error formulation and a generalized probabilistic descent (GPD) algorithm are analyzed and used to accomplish discriminative training of a conventional dynamic-programming-based speech recognizer. The objective of discriminative training here is to directly minimize the recognition error rate. To achieve this, a formulation that allows controlled approximation of the exact error rate and renders optimization possible is used. The GPD method is implemented in a dynamic-time-warping (DTW)-based system. A linear discriminant function on the DTW distortion sequence is used to replace the conventional average DTW path distance. A series of speaker-independent recognition experiments using the highly confusible English E-set as the vocabulary showed a recognition rate of 84.4% compared to approximately 60% for traditional template training via clustering. The experimental results verified that the algorithm converges to a solution that achieves minimum error rate. >

On the hidden Markov model and dynamic time warping for speech recognition — A unified view

Abstract: This paper gives a unified theoretical view of the Dynamic Time Warping (DTW) and the Hidden Markov Model (HMM) techniques for speech recognition problems. The application of hidden Markov models in speech recognition is discussed. We show that the conventional dynamic time-warping algorithm with Linear Predictive (LP) signal modeling and distortion measurements can be formulated in a strictly statistical framework. It is further shown that the DTW/LP method is implicitly associated with a specific class of Markov models and is equivalent to the probability maximization procedures for Gaussian autoregressive multivariate probabilistic functions of the underlying Markov model. This unified view offers insights into the effectiveness of the probabilistic models in speech recognition applications.