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.
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TL;DR: The results demonstrate that the gesture control with tactile feedback is a promising technology compared to a vision-based gesture control system that has typically no feedback for the user's gesture inputs.
Abstract: Vision-based hand gesture interactions are natural and intuitive when interacting with computers, since we naturally exploit gestures to communicate with other people. However, it is agreed that users suffer from discomfort and fatigue when using gesture-controlled interfaces, due to the lack of physical feedback. To solve the problem, we propose a novel complete solution of a hand gesture control system employing immersive tactile feedback to the user's hand. For this goal, we first developed a fast and accurate hand-tracking algorithm with a Kinect sensor using the proposed MLBP (modified local binary pattern) that can efficiently analyze 3D shapes in depth images. The superiority of our tracking method was verified in terms of tracking accuracy and speed by comparing with existing methods, Natural Interaction Technology for End-user (NITE), 3D Hand Tracker and CamShift. As the second step, a new tactile feedback technology with a piezoelectric actuator has been developed and integrated into the developed hand tracking algorithm, including the DTW (dynamic time warping) gesture recognition algorithm for a complete solution of an immersive gesture control system. The quantitative and qualitative evaluations of the integrated system were conducted with human subjects, and the results demonstrate that our gesture control with tactile feedback is a promising technology compared to a vision-based gesture control system that has typically no feedback for the user's gesture inputs. Our study provides researchers and designers with informative guidelines to develop more natural gesture control systems or immersive user interfaces with haptic feedback.
42 citations
TL;DR: In this article, radial basis functions (RBF) are used to model and extract stylistic and affective features from motion data, and an ensemble of RBF neural networks is employed to learn the affective/stylistic features following time warping and principal component analysis.
42 citations
TL;DR: Dynamic time warping is a procedure whereby portions of a temporal sequence of values are stretched or shrunk to make it similar to another sequence to align the brain-stem auditory evoked potentials recorded from different subjects prior to averaging.
42 citations
14 Mar 2010
TL;DR: This paper presents an “unbounded” version on the DTW (U-DTW in short) that is computationally lightweight and allows for total flexibility on where the matching segment occurs.
Abstract: Before the advent of Hidden Markov Models(HMM)-based speech recognition, many speech applications were built using pattern matching algorithms like the Dynamic Time Warping (DTW) algorithm, which are generally robust to noise and easy to implement. The standard DTW algorithm usually suffers from lack of flexibility on start-end matching points and has high computational costs. Although some DTW-based algorithms have been proposed over the years to solve either one of these problems, none is able to discover multiple alignment paths with low computational costs. In this paper, we present an “unbounded” version on the DTW (U-DTW in short) that is computationally lightweight and allows for total flexibility on where the matching segment occurs. Results on a word matching database show very competitive performances both in accuracy and processing time compared to existing alternatives.
42 citations
01 Jan 2007
TL;DR: In this paper, the authors discuss the strengths and weaknesses of SVMs in the ASR context and make a review of the current state-of-the-art techniques for isolated-word recognition and continuous speech recognition.
Abstract: Hidden Markov Models (HMMs) are, undoubtedly, the most employed core technique for Automatic Speech Recognition (ASR). Nevertheless, we are still far from achieving high-performance ASR systems. Some alternative approaches, most of them based on Artificial Neural Networks (ANNs), were proposed during the late eighties and early nineties. Some of them tackled the ASR problem using predictive ANNs, while others proposed hybrid HMM/ANN systems. However, despite some achievements, nowadays, the preponderance of Markov Models is a fact.
During the last decade, however, a new tool appeared in the field of machine learning that has proved to be able to cope with hard classification problems in several fields of application: the Support Vector Machines (SVMs). The SVMs are effective discriminative classifiers with several outstanding characteristics, namely: their solution is that with maximum margin; they are capable to deal with samples of a very higher dimensionality; and their convergence to the minimum of the associated cost function is guaranteed.
These characteristics have made SVMs very popular and successful. In this chapter we discuss their strengths and weakness in the ASR context and make a review of the current state-of-the-art techniques. We organize the contributions in two parts: isolated-word recognition and continuous speech recognition. Within the first part we review several techniques to produce the fixed-dimension vectors needed for original SVMs. Afterwards we explore more sophisticated techniques based on the use of kernels capable to deal with sequences of different length. Among them is the DTAK kernel, simple and effective, which rescues an old technique of speech recognition: Dynamic Time Warping (DTW). Within the second part, we describe some recent approaches to tackle more complex tasks like connected digit recognition or continuous speech recognition using SVMs. Finally we draw some conclusions and outline several ongoing lines of research.
42 citations