Robust features for automatic estimation of physical parameters from speech

Simple item page
dc.contributor.authorKalluri, K.S.
dc.contributor.authorVijayasenan, D.
dc.date.accessioned2026-02-06T06:38:33Z
dc.date.issued2017
dc.description.abstractEstimating speaker's physical parameters like height, weight and shoulder size can assist in voice forensics by providing additional knowledge about the speaker. In this work, statistics of the components of background GMM are employed as features in estimating the physical parameters. These features improved the performance of height and shoulder size estimation as compared to our earlier attempt based on a Bag of Word representation. The robustness of the features is validated using two different training subsets containing different languages. © 2017 IEEE.
dc.identifier.citationIEEE Region 10 Annual International Conference, Proceedings/TENCON, 2017, Vol.2017-December, , p. 1515-1519
dc.identifier.issn21593442
dc.identifier.urihttps://doi.org/10.1109/TENCON.2017.8228097
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/31722
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectfirst order statistics
dc.subjectGMM-UBM
dc.subjectheight
dc.subjectMFCC
dc.subjectPhysical parameters
dc.subjectshoulder size
dc.subjectSpeech forensics
dc.subjectSVR
dc.subjectweight
dc.titleRobust features for automatic estimation of physical parameters from speech

Files

Collections

Conference Papers