scispace - formally typeset
Search or ask a question
Topic

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.


Papers
More filters
Book ChapterDOI
15 Apr 1996
TL;DR: Tests on small isolated-word vocabularies using a dynamic time warping based audio-visual recogniser demonstrate that real-time, contour-based lip tracking can be used to supplement acoustic-only speech recognisers enabling robust recognition of speech in the presence of acoustic noise.
Abstract: Developments in dynamic contour tracking permit sparse representation of the outlines of moving contours. Given the increasing computing power of general-purpose workstations it is now possible to track human faces and parts of faces in real-time without special hardware. This paper describes a real-time lip tracker that uses a Kalman filter based dynamic contour to track the outline of the lips. Two alternative lip trackers, one that tracks lips from a profile view and the other from a frontal view, were developed to extract visual speech recognition features from the lip contour. In both cases, visual features have been incorporated into an acoustic automatic speech recogniser. Tests on small isolated-word vocabularies using a dynamic time warping based audio-visual recogniser demonstrate that real-time, contour-based lip tracking can be used to supplement acoustic-only speech recognisers enabling robust recognition of speech in the presence of acoustic noise.

96 citations

Journal ArticleDOI
TL;DR: Experimental results have successfully validated the effectiveness of the DTW-based recognition algorithm for online handwriting and gesture recognition using the inertial pen.
Abstract: This paper presents an inertial-sensor-based digital pen (inertial pen) and its associated dynamic time warping (DTW)-based recognition algorithm for handwriting and gesture recognition. Users hold the inertial pen to write numerals or English lowercase letters and make hand gestures with their preferred handheld style and speed. The inertial signals generated by hand motions are wirelessly transmitted to a computer for online recognition. The proposed DTW-based recognition algorithm includes the procedures of inertial signal acquisition, signal preprocessing, motion detection, template selection, and recognition. We integrate signals collected from an accelerometer, a gyroscope, and a magnetometer into a quaternion-based complementary filter for reducing the integral errors caused by the signal drift or intrinsic noise of the gyroscope, which might reduce the accuracy of the orientation estimation. Furthermore, we have developed a minimal intra-class to maximal inter-class based template selection method (min-max template selection method) for a DTW recognizer to obtain a superior class separation for improved recognition. Experimental results have successfully validated the effectiveness of the DTW-based recognition algorithm for online handwriting and gesture recognition using the inertial pen.

96 citations

Proceedings ArticleDOI
02 Sep 2002
TL;DR: This work proposes the use of non-metric distance functions based on the longest common subsequence (LCSS), in conjunction with a sigmoidal matching function for similarity analysis of spatio-temporal trajectories for mobile objects.
Abstract: We investigate techniques for similarity analysis of spatio-temporal trajectories for mobile objects. Such data may contain a large number of outliers, which degrade the performance of Euclidean and time warping distance. Therefore, we propose the use of non-metric distance functions based on the longest common subsequence (LCSS), in conjunction with a sigmoidal matching function. Finally, we compare these new methods to various L/sub p/ norms and also to time warping distance (for real and synthetic data) and present experimental results that validate the accuracy and efficiency of our approach, especially in the presence of noise.

95 citations

Book ChapterDOI
01 Jan 2008
TL;DR: The intrinsic dependence that the lexical content of the password phrase has on the accuracy is demonstrated and several research results will be presented and analyzed to show key techniques used in text-dependent speaker recognition systems from different sites.
Abstract: Text-dependent speaker recognition characterizes a speaker recognition task, such as verification or identification, in which the set of words (or lexicon) used during the testing phase is a subset of the ones present during the enrollment phase. The restricted lexicon enables very short enrollment (or registration) and testing sessions to deliver an accurate solution but, at the same time, represents scientific and technical challenges. Because of the short enrollment and testing sessions, text-dependent speaker recognition technology is particularly well suited for deployment in large-scale commercial applications. These are the bases for presenting an overview of the state of the art in text-dependent speaker recognition as well as emerging research avenues. In this chapter, we will demonstrate the intrinsic dependence that the lexical content of the password phrase has on the accuracy. Several research results will be presented and analyzed to show key techniques used in text-dependent speaker recognition systems from different sites. Among these, we mention multichannel speaker model synthesis and continuous adaptation of speaker models with threshold tracking. Since text-dependent speaker recognition is the most widely used voice biometric in commercial deployments, several

94 citations

Book ChapterDOI
20 Oct 2007
TL;DR: This work demonstrates the applicability of Isomap for dimensionality reduction in human motion recognition and shows how an adapted dynamic time warping algorithm (DTW) can be successfully used for matching motion patterns of embedded manifolds.
Abstract: In this paper, we address the problem of recognizing human motion from videos. Human motion recognition is a challenging computer vision problem. In the past ten years, a number of successful approaches based on nonlinear manifold learning have been proposed. However, little attention has been given to the use of isometric feature mapping (Isomap) for human motion recognition. Our contribution in this paper is twofold. First, we demonstrate the applicability of Isomap for dimensionality reduction in human motion recognition. Secondly, we show how an adapted dynamic time warping algorithm (DTW) can be successfully used for matching motion patterns of embedded manifolds. We compare our method to previous works on human motion recognition. Evaluation is performed utilizing an established baseline data set from the web for direct comparison. Finally, our results show that our Isomap-DTW method performs very well for human motion recognition.

94 citations


Network Information
Related Topics (5)
Feature extraction
111.8K papers, 2.1M citations
91% related
Convolutional neural network
74.7K papers, 2M citations
87% related
Deep learning
79.8K papers, 2.1M citations
87% related
Image segmentation
79.6K papers, 1.8M citations
86% related
Artificial neural network
207K papers, 4.5M citations
84% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023236
2022471
2021341
2020416
2019420
2018377