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.

Preventing replay attacks on speaker verification systems

Abstract: In this paper, we describe a system for detecting spoofing attacks on speaker verification systems. We understand as spoofing the fact of impersonating a legitimate user. We focus on detecting two types of low technology spoofs. On the one side, we try to expose if the test segment is a far-field microphone recording of the victim that has been replayed on a telephone handset using a loudspeaker. On the other side, we want to determine if the recording has been created by cutting and pasting short recordings to forge the sentence requested by a text dependent system. This kind of attacks is of critical importance for security applications like access to bank accounts. To detect the first type of spoof we extract several acoustic features from the speech signal. Spoofs and non-spoof segments are classified using a support vector machine (SVM). The cut and paste is detected comparing the pitch and MFCC contours of the enrollment and test segments using dynamic time warping (DTW). We performed experiments using two databases created for this purpose. They include signals from land line and GSM telephone channels of 20 different speakers. We present results of the performance separately for each spoofing detection system and the fusion of both. We have achieved error rates under 10% for all the conditions evaluated. We show the degradation on the speaker verification performance in the presence of this kind of attack and how to use the spoofing detection to mitigate that degradation.

Generating Synthetic Time Series to Augment Sparse Datasets

Abstract: In machine learning, data augmentation is the process of creating synthetic examples in order to augment a dataset used to learn a model. One motivation for data augmentation is to reduce the variance of a classifier, thereby reducing error. In this paper, we propose new data augmentation techniques specifically designed for time series classification, where the space in which they are embedded is induced by Dynamic Time Warping (DTW). The main idea of our approach is to average a set of time series and use the average time series as a new synthetic example. The proposed methods rely on an extension of DTW Barycentric Averaging (DBA), the averaging technique that is specifically developed for DTW. In this paper, we extend DBA to be able to calculate a weighted average of time series under DTW. In this case, instead of each time series contributing equally to the final average, some can contribute more than others. This extension allows us to generate an infinite number of new examples from any set of given time series. To this end, we propose three methods that choose the weights associated to the time series of the dataset. We carry out experiments on the 85 datasets of the UCR archive and demonstrate that our method is particularly useful when the number of available examples is limited (e.g. 2 to 6 examples per class) using a 1-NN DTW classifier. Furthermore, we show that augmenting full datasets is beneficial in most cases, as we observed an increase of accuracy on 56 datasets, no effect on 7 and a slight decrease on only 22.

A Framework for the Recognition of High-Level Surgical Tasks From Video Images for Cataract Surgeries

Abstract: The need for a better integration of the new generation of computer-assisted-surgical systems has been recently emphasized. One necessity to achieve this objective is to retrieve data from the operating room (OR) with different sensors, then to derive models from these data. Recently, the use of videos from cameras in the OR has demonstrated its efficiency. In this paper, we propose a framework to assist in the development of systems for the automatic recognition of high-level surgical tasks using microscope videos analysis. We validated its use on cataract procedures. The idea is to combine state-of-the-art computer vision techniques with time series analysis. The first step of the framework consisted in the definition of several visual cues for extracting semantic information, therefore, characterizing each frame of the video. Five different pieces of image-based classifiers were, therefore, implemented. A step of pupil segmentation was also applied for dedicated visual cue detection. Time series classification algorithms were then applied to model time-varying data. Dynamic time warping and hidden Markov models were tested. This association combined the advantages of all methods for better understanding of the problem. The framework was finally validated through various studies. Six binary visual cues were chosen along with 12 phases to detect, obtaining accuracies of 94%.

Comparing gesture recognition accuracy using color and depth information

Abstract: In human-computer interaction applications, gesture recognition has the potential to provide a natural way of communication between humans and machines. The technology is becoming mature enough to be widely available to the public and real-world computer vision applications start to emerge. A typical example of this trend is the gaming industry and the launch of Microsoft's new camera: the Kinect. Other domains, where gesture recognition is needed, include but are not limited to: sign language recognition, virtual reality environments and smart homes. A key challenge for such real-world applications is that they need to operate in complex scenes with cluttered backgrounds, various moving objects and possibly challenging illumination conditions. In this paper we propose a method that accommodates such challenging conditions by detecting the hands using scene depth information from the Kinect. On top of our detector we employ a dynamic programming method for recognizing gestures, namely Dynamic Time Warping (DTW). Our method is translation and scale invariant which is a desirable property for many HCI systems. We have tested the performance of our approach on a digits recognition system. All experimental datasets include hand signed digits gestures but our framework can be generalized to recognize a wider range of gestures.

Rapid Evaluation of Speech Representations for Spoken Term Discovery.

Abstract: Acoustic front-ends are typically developed for supervised learning tasks and are thus optimized to minimize word error rate, phone error rate, etc. However, in recent efforts to develop zero-resource speech technologies, the goal is not to use transcribed speech to train systems but instead to discover the acoustic structure of the spoken language automatically. For this new setting, we require a framework for evaluating the quality of speech representations without coupling to a particular recognition architecture. Motivated by the spoken term discovery task, we present a dynamic time warping-based framework for quantifying how well a representation can associate words of the same type spoken by different speakers. We benchmark the quality of a wide range of speech representations using multiple frame-level distance metrics and demonstrate that our performance metrics can also accurately predict phone recognition accuracies.