We present a new space-efficient approach, (SparseDTW ), to compute the Dynamic Time Warping (DTW ) distance between two time series that always yields the optimal result. This is in contrast to other known approaches which typically sacrifice optimality to attain space efficiency. The main idea behind our approach is to dynamically exploit the existence of similarity and/or correlation between...

Emotion recognition from speech has emerged as an important research area in the recent past. The purpose of speech emotion recognition system is to automatically classify speaker's utterances into seven emotional states including anger, boredom, disgust, fear, happiness, sadness and neutral. The speech samples are from Berlin emotional database and the features extracted from these uttera...

This paper describes a new formulation of a polynomial sequence kernel based on dynamic time warping (DTW) for support vector machine (SVM) classification of isolated words given very sparse training data. The words are uttered by dysarthric speakers who suffer from debilitating neurological conditions that make the collection of speech samples a timeconsuming and low-yield process. Data for bu...

This paper evaluates continuous density hidden Markov models (CDHMM), dynamic time warping (DTW) and distortion-based vector quantisa-tion (VQ) for speaker recognition, across incremen-tal amounts of training data. In comparing VQ and CDHMMs for text-independent (TI) speaker recognition , it is shown that VQ performs better than an equivalent CDHMM with one training version, but is outperformed...

This paper discusses the use of Dynamic Time Warping (DTW) for visually perceptive and intuitive character recognition. In particular in forensic document examination, techniques are required that yield results matching a human user’s expectations. In our approach, the goal is to retrieve a set of best matching allographic prototypes based on a query input character. Since DTW compares each pai...

This paper presents the pre-processing of speech templates for artificial neural network (ANN). The processed features are pitch and Linear Predictive Coefficients (LPC) for input and reference templates, based on Dynamic Time Warping (DTW) algorithm. The first task is to extract pitch features using Pitch Scale Harmonic Filter algorithm. Another task is to align the input frames (test set) to ...

This paper presents a procedure of frame normalization based on the traditional dynamic time warping (DTW) using the LPC coefficients. The redefined method is called as the DTW frame-fixing method (DTW-FF), it works by normalizing the word frames of the input against the reference frames. The enthusiasm to this study is due to neural network limitation that entails a fix number of input nodes f...

The purpose of this paper is to present a Dynamic Time Warping technique which reduces significantly the data processing time and memory size of multi-dimensional time series sampled by the biometric smart pen device BiSP. The acquisition device is a novel ballpoint pen equipped with a diversity of sensors for monitoring the kinematics and dynamics of handwriting movement. The DTW algorithm has...

This study investigates the characteristics and prediction of the Maritime Continent (MC) rainfall for the transitional periods between wet and dry seasons. Several observational data sets and the output from the 45-day hindcast by the U.S. National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) are used. Results show that the MC experiences a sudden trans...

Dynamic Time Warping (DTW) and Geometric Edit Distance (GED) are basic similarity measures between curves or general temporal sequences (e.g., time series) that are represented as sequences of points in some metric space (X, dist). The DTW and GED measures are massively used in various fields of computer science and computational biology, consequently, the tasks of computing these measures are ...