Create adjacency matrices of vocalisation graphs from
dataframes containing sequences of speech and silence intervals,
transforming these matrices into Markov diagrams, and generating
datasets for classification of these diagrams by 'flattening' them
and adding global properties (functionals) etc. Vocalisation
diagrams date back to early work in psychiatry (Jaffe and Feldstein,
1970) and social psychology (Dabbs and Ruback, 1987) but have only
recently been employed as a data representation method for machine
learning tasks including meeting segmentation (Luz, 2012)
|Author||Saturnino Luz [aut, cre]|
|Date of publication||2017-08-10 15:12:04 UTC|
|Maintainer||Saturnino Luz <[email protected]>|
|Package repository||View on CRAN|
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