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Maximum a Posteriori Pitch Tracking

Jasha Droppo and Alex Acero

Abstract

A Maximum a posteriori framework for computing pitch tracks as well as voicing decisions is presented. The proposed algorithm consists of creating a time-pitch energy distribution based on predictable energy that improves on the normalized cross-correlation. A large database is used to evaluate the algorithm’s performance against two standard solutions, using glottal closure instants (GCI) obtained from electroglottogram (EGG) signals as a reference. The new MAP algorithm exhibits higher pitch accuracy and better voiced/unvoiced discrimination.

Details

Publication typeInproceedings
Published inProc. International Conference on Spoken Language Processing
AddressSydney, Australia
PublisherInternational Speech Communication Association
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