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.
Cite as: Droppo, J., Acero, A. (1998) Maximum a posteriori pitch tracking. Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998), paper 0071, doi: 10.21437/ICSLP.1998-509
@inproceedings{droppo98_icslp,
author={James Droppo and Alex Acero},
title={{Maximum a posteriori pitch tracking}},
year=1998,
booktitle={Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998)},
pages={paper 0071},
doi={10.21437/ICSLP.1998-509}
}