TY - JOUR
T1 - Automatic measurement of positive and negative voice onset time
AU - Henry, Katharine
AU - Sonderegger, Morgan
AU - Keshet, Joseph
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Previous work on automatic VOT measurement has focused on positive-valued VOT. However, in many languages VOT can be either positive or negative ("prevoiced"). We present a discriminative algorithm that simultaneously decides whether a stop is prevoiced and measures its VOT. The algorithm operates on feature functions designed to locate the burst and voicing onsets in the positive and negative VOT cases. Tested on a database of positive- and negative-VOT voiced stops, the algorithm predicts prevoicing with >90% accuracy, and gives good agreement between automatic and manual measurements.
AB - Previous work on automatic VOT measurement has focused on positive-valued VOT. However, in many languages VOT can be either positive or negative ("prevoiced"). We present a discriminative algorithm that simultaneously decides whether a stop is prevoiced and measures its VOT. The algorithm operates on feature functions designed to locate the burst and voicing onsets in the positive and negative VOT cases. Tested on a database of positive- and negative-VOT voiced stops, the algorithm predicts prevoicing with >90% accuracy, and gives good agreement between automatic and manual measurements.
UR - https://www.scopus.com/pages/publications/84878406359
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VL - 1
JO - 13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012
JF - 13th Annual Conference of the International Speech Communication Association 2012, INTERSPEECH 2012
ER -