This paper describes a methodology based on DTW technique (Dynamic Time Warping) applied to vocal audiometry to classify the different pathologies of hearing impaired. This methodology is validated on a population of ten subjects composed of seven individuals suffering perception, transmission or mixed deafness and three nondisabled subjects. The obtained results show that this method can be us...

The main objective of the study is to assess similarity between time series energy commodity prices and daily COVID-19 cases. pandemic affects all aspects global economy. Although this impact multifaceted, we connections number cases commodities sector. We analyse these by using Dynamic Time Warping (DTW) method. On basis, calculate measure—the DTW distance series—and use it group according the...

We introduce an approach for model-based sequence clustering that addresses several drawbacks of existing algorithms. The approach uses a combination of Hidden Markov Modeling (HMM) for sequence estimation and Dynamic Time Warping (DTW) for hierarchical clustering, with interlocking steps of model selection, estimation and sequence grouping. We demonstrate experimentally that the algorithm can ...

Recognizing clinical style is essential for generating intelligent guidance in virtual reality simulators for dental skill acquisition. The aim of this study was to determine the potential of Dynamic Time Warping (DTW) in matching novices' tooth cutting sequences with those of experts. Forty dental students and four expert dentists were enrolled to perform access opening to the root canals with...

For query-by-example spoken term detection (QbE-STD) on low resource languages, variants of dynamic time warping techniques (DTW) are used. However, DTW-based techniques are slow and thus a limitation to search in large spoken audio databases. In order to enable fast search in large databases, we exploit the use of intensive parallel computations of the graphical processing units (GPUs). In thi...

To prepare the age when humans and robots live together, robots need to understand the meaning of human behaviors for the natural and rational human-robot interaction (HRI). The robot particularly needs to recognize the human hands behavior, since humans usually express their meanings and intentions by using two hands. In this paper, the robot recognizes the human hands behavior by simulating i...

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...

Methods for measuring latency contrasts are evaluated against a new method utilizing the Dynamic Time Warping algorithm. They are applied on simulated data, for different signal to noise ratios and two sizes of window (broad vs narrow). The results are subjected to statistical and ROC analysis. The analysis suggests that DTW performs better than the other methods, being less sensitive to noise ...