Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Feature Trajectory Dynamic Time Warping for Clustering of Speech
  Segments   Dynamic time warping (DTW) can be used to compute the similarity between two
sequences of generally differing length. We propose a modification to DTW that
performs individual and independent pairwise alignment of feature trajectories.
The modified technique, termed feature trajectory dynamic time warping (FTDTW),
is applied as a similarity measure in the agglomerative hierarchical clustering
of speech segments. Experiments using MFCC and PLP parametrisations extracted
from TIMIT and from the Spoken Arabic Digit Dataset (SADD) show consistent and
statistically significant improvements in the quality of the resulting clusters
in terms of F-measure and normalised mutual information (NMI).

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The categories of this paper are: econ
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-bio
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The categories of this paper are: physics
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The categories of this paper are: supr-con
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The categories of this paper are: patt-sol