The primary system we submitted was composed of 11 subsystems as the required run. 3 subsystems are based on Acoustic Keyword Spotting (AKWS) and 8 on Dynamic Time Warping (DTW). The AKWS systems were based only on phoneme posteriors while the DTW subsystems were based on both phoneme posteriors and Bottle-Neck features (BN) as input. The underlying phoneme posterior estimators / bottle-neck fe...

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 ...

The study proposes an algorithm for noise cancellation by using recursive least square (RLS) and pattern recognition by using fusion method of Dynamic Time Warping (DTW) and Hidden Markov Model (HMM). Speech signals are often corrupted with background noise and the changes in signal characteristics could be fast. These issues are especially important for robust speech recognition. Robustness is...

There has been much recent interest in adapting data mining algorithms to time series databases. Many of these algorithms need to compare time series. Typically some variation or extension of Euclidean distance is used. However, as we demonstrate in this paper, Euclidean distance can be an extremely brittle distance measure. Dynamic time warping (DTW) has been suggested as a technique to allow ...

There has been much recent interest in adapting data mining algorithms to time series databases. Many of these algorithms need to compare time series. Typically some variation or extension of Euclidean distance is used. However, as we demonstrate in this paper, Euclidean distance can be an extremely brittle distance measure. Dynamic time warping (DTW) has been suggested as a technique to allow ...

Time series averaging in dynamic time warping (DTW) spaces has been successfully applied to improve pattern recognition systems. This article proposes and analyzes subgradient methods for the problem of finding a sample mean in DTW spaces. The class of subgradient methods generalizes existing sample mean algorithms such as DTW Barycenter Averaging (DBA). We show that DBA is a majorize-minimize ...

Analyzing multiple microarray experimental data sets, which are comprised of levels of gene expression, induces several challenges. Of particular interest to biologists are genes that are differentially expressed, that is, significantly altered up or down levels over time, which may lead to identification of disease involved genes. Dynamic Time Warping (DTW) is a computational method that has t...

This paper describes our ’any-time’ real-time music tracking system which is based on an on-line version of the well-known Dynamic Time Warping (DTW) algorithm and includes some extensions to improve both the precision and the robustness of the alignment (e.g. a tempo model and the ability to reconsider past decisions). A unique feature of our system is the ability to cope with arbitrary struct...

When comparing time series, z-normalization preprocessing and dynamic time warping (DTW) distance became almost standard procedure. This paper makes a point against carelessly using this setup by discussing implications and alternatives. A (conceptually) simpler distance measure is proposed that allows for a linear transformation of amplitude and time only, but is also open for other normalizat...

Dynamic Time Warping (DTW) is one of the basic similarity measures between curves or general temporal sequences (e.g., time series) that are represented as sequence of points in some metric space pX, distq. The DTW measure is massively used in many practical fields of computer science, and computing the DTW between two sequences is a classical problem in P. Despite extensive efforts to find mor...