Speaker Identification Experiments Under Gender De-Identification
Sound
2022-04-13 v1 Machine Learning
Audio and Speech Processing
Abstract
The present work is based on the COST Action IC1206 for De-identification in multimedia content. It was performed to test four algorithms of voice modifications on a speech gender recognizer to find the degree of modification of pitch when the speech recognizer have the probability of success equal to the probability of failure. The purpose of this analysis is to assess the intensity of the speech tone modification, the quality, the reversibility and not-reversibility of the changes made. Keywords DeIdentification; Speech Algorithms
Keywords
Cite
@article{arxiv.2203.04638,
title = {Speaker Identification Experiments Under Gender De-Identification},
author = {Marcos Faundez-Zanuy and Enric Sesa-Nogueras and Stefano Marinozzi},
journal= {arXiv preprint arXiv:2203.04638},
year = {2022}
}
Comments
5 pages. arXiv admin note: substantial text overlap with arXiv:2203.03932