Book ChapterDOI
Dynamic Time Warping
Alan Bundy,Lincoln Wallen +1 more
- pp 32-33
TLDR
Two programs are provided, one that generates Ipc and autocorrelation coefficients from the speech utterances and the other that, using dynamic programming, compares the test utterance with the reference utterance and finds the best match.Abstract:
Two programs are provided, one that generates Ipc and autocorrelation coefficients from the speech utterances and the other that, using dynamic programming, compares the test utterance with the reference utterances and finds the best match. The method used is Constrained Endpoint with 2-to-l range of slope.read more
Citations
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Towards a Reliable Intrusion Detection Benchmark Dataset
TL;DR: A comprehensive evaluation of the existing datasets using the proposed criteria, a design and evaluation framework for IDS and IPS datasets, and a dataset generation model to create a reliable IDS or IPS benchmark dataset are presented.
Proceedings ArticleDOI
AVEC 2018 Workshop and Challenge: Bipolar Disorder and Cross-Cultural Affect Recognition
Fabien Ringeval,Björn Schuller,Michel Valstar,Roddy Cowie,Heysem Kaya,Maximilian Schmitt,Shahin Amiriparian,Nicholas Cummins,Denis Lalanne,Adrien Michaud,Elvan Ciftci,Hüseyin Güleç,Albert Ali Salah,Maja Pantic +13 more
TL;DR: This paper presents the major novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline systems on the three proposed tasks: bipolar disorder classification, cross-cultural dimensional emotion recognition, and emotional label generation from individual ratings, respectively.
Proceedings Article
Gesture Recognition using Skeleton Data with Weighted Dynamic Time Warping
TL;DR: This work proposes a weighted DTW method that weights joints by optimizing a discriminant ratio and demonstrates the recognition performance of the proposed weightedDTW with respect to the conventional DTW and state-of-the-art Kinect.
References
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Journal ArticleDOI
Considerations in dynamic time warping algorithms for discrete word recognition
TL;DR: It is shown that, based on a set of assumptions about the distributions of the distances, the warping algorithm that minimizes the overall probability of making a word error is the modified time Warping algorithm with unconstrained endpoints.