Feature trajectory dynamic time warping for clustering of speech segments
نویسندگان
چکیده
منابع مشابه
Adaptive Feature Based Dynamic Time Warping
Dynamic time warping (DTW) has been widely used in various pattern recognition and time series data mining applications. However, as examples will illustrate, both the classic DTW and its later alternative, derivative DTW, may fail to align a pair of sequences on their common trends or patterns. Furthermore, the learning capability of any supervised learning algorithm based on classic/derivativ...
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Context dependent modelling is known to improve recognition performance for automatic speech recognition. One of the major limitations, especially of approaches based on Decision Trees, is that the questions that guide the search for effective contexts must be known in advance. However, the variation in the speech signals is caused by multiple factors, not all of which may be known during the t...
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An intrinsic problem of classifiers based on machine learning (ML) methods is that their learning time grows as the size and complexity of the training dataset increases. For this reason, it is important to have efficient computational methods and algorithms that can be applied on large datasets, such that it is still possible to complete the machine learning tasks in reasonable time. In this c...
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Time series are a ubiquitous form of data occurring in virtually every scientific discipline. A common task with time series data is comparing one sequence with another. In some domains a very simple distance measure, such as Euclidean distance will suffice. However, it is often the case that two sequences have the approximately the same overall component shapes, but these shapes do not line up...
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ژورنال
عنوان ژورنال: EURASIP Journal on Audio, Speech, and Music Processing
سال: 2019
ISSN: 1687-4722
DOI: 10.1186/s13636-019-0149-9