Robust features for automatic estimation of physical parameters from speech

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Date

2017

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Institute of Electrical and Electronics Engineers Inc.

Abstract

Estimating 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.

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Keywords

first order statistics, GMM-UBM, height, MFCC, Physical parameters, shoulder size, Speech forensics, SVR, weight

Citation

IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2017, Vol.2017-December, , p. 1515-1519

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