In this paper we study the eeect of vocal tract normalization (VTN) on the word error rate (WER) in speaker independent large vocabulary speech recognition. Evaluation test results are reported for the German VerbMobil II (VM II) and the English Wall Street Journal (WSJ) corpus. In particular, we analyse: the eeect of the type of warping function (linear vs. non-linear) on the WER; diierent met...

In this work we extend the test utterance adaptation technique used in vocal tract length normalization to a larger number of speaker characteristic features. We perform partially joint estimation of four features: the VTLN warping factor, the corner position of the piece-wise linear warping function, spectral tilt in voiced segments, and model variance scaling. In experiments on the Swedish PF...

In this paper, a new warping technique called contour-based warping is presented. Feature contours of objects are de ned and mapped to their target shapes. This allows the user greater exibility in de ning the warping with minimal e ort. Two image warping methods are introduced in this paper and both are based on the concept of mapping contours. The peel-and-resample method can warp simple imag...

A warping operator consists of an invertible axisdeformation applied either in the signal domain or in thecorresponding Fourier domain. Additionally, a warping trans-formation is usually required to preserve the signal energy, thuspreserving orthogonality and being invertible by its adjoint.Initially, the design of such operators has been motivated bythe idea of suitably...

A new efficient two-dimensional warping algorithm is presented, in which sub-optimal warping is attained by iterating DP-based local optimization of warp on partially overlapping subplane sequence. From an experimental comparison with a conventional approximation algorithm based on beam search DP, relative superiority of the proposed algorithm is established. key words: two-dimensional warping,...

While there exist a plethora of classification algorithms for most data types, there is an increasing acceptance that the unique properties of time series mean that the combination of nearest neighbor classifiers and Dynamic Time Warping (DTW) is very competitive across a host of domains, from medicine to astronomy to environmental sensors. While there has been significant progress in improving...

An accurate comparison of multimodal and/or interindividual 3D image datasets of brains requires geometric transformation techniques (warping) to reduce geometric variations. Here, a subset of warping techniques, namely point-based warping, is investigated. For this kind of warping landmarks between datasets have to be defined. In large 3D datasets manually setting of landmarks is timeconsuming...

Dynamic time warping (DTW) is a fundamental technique in time series analysis for comparing one curve to another using a flexible time-warping function. However, it was designed to compare a single pair of curves. In many applications, such as in metabolomics and image series analysis, alignment is simultaneously needed for multiple pairs. Because the underlying warping functions are often rela...

Given two time series, possibly of different lengths, time warping is a method to construct an optimal alignment obtained by stretching or contracting time intervals. Unlike pairwise alignment of amino acid sequences, classical time warping, originally introduced for speech recognition, is not symmetric in the sense that the time warping distance between two time series is not necessarily equal...