Dynamic time warping (DTW) is a successful algorithm in many matching and searching tasks. For the text-dependent speaker verification, it is still an appropriate choice when enrollment data are very limited. Yet DTW is very sensitive to the endpoint variations between the reference template and test examples. Most research reported on this issue is mainly in two directions: robust endpoint det...

Common approaches to automatic speech recognition (ASR) are based on training statistical models for the acoustics of speech. In our work, a retrieval-based ASR system is developed that does not rely on training and thus provides more flexible application. It is based on a set of known reference word utterances for each possibly occurring word in a test string. A test word string is identified ...

This paper introduces a method for human action recognition based on optical flow motion features extraction. Automatic spatial and temporal alignments are combined together in order to encourage the temporal consistence on each action by an enhanced dynamic time warping (DTW) algorithm. At the same time, a fast method based on coarse-to-fine DTW constraint to improve computational performance ...

Data streams are pervasive in many modern applications, and there is a pressing need to develop techniques for their efficient management. In this paper we consider real-valued streams and deal with the problem of reporting in real-time all the instants in which their distance falls below a given threshold. Current distance measures, such as Euclidean and Dynamic Time Warping (DTW ), either are...

A method commonly used to “time-normalize” gait data (here referred to as linear length normalization [LLN]) is to linearly convert the trajectory’s time axis from the experimentally-recorded time units to an axis representing percentage of the gait cycle. However, other time-normalization techniques are also possible, such as dynamic time warping [DTW] and derivative dynamic time warping [DDTW...

We present two heuristics for speeding up a time series alignment algorithm that is related to dynamic time warping (DTW). In previous work, we developed our multisegment alignment algorithm to answer similarity queries for toxicogenomic time-series data. Our multisegment algorithm returns more accurate alignments than DTW at the cost of time complexity; the multisegment algorithm is O(n(5)) wh...

This paper presents the use of Dynamic Time Warping (DTW) for measuring prosodic differences between variable-sized sentences. This methodological study may apply to various prosodic functions, accented or expressive speech. Both the structuring and attitudinal functions of prosody are investigated here. We evaluated the relevance of three prosodic (dis)similarity measures to account for percei...

The Dynamic Time Warping (DTW) is a popular similarity measure between time series. The DTW fails to satisfy the triangle inequality and its computation requires quadratic time. Hence, to find closest neighbors quickly, we use bounding techniques. We can avoid most DTW computations with an inexpensive lower bound (LB Keogh). We compare LB Keogh with a tighter lower bound (LB Improved). We find ...

Dynamic Time Warping (DTW) has been widely used in time series domain as a distance function for similarity search. Several works have utilized DTW to improve the classification accuracy as it can deal with local time shiftings in time series data by non-linear warping. However, some types of time series data do have several segments that one segment should not be compared to others even though...