Skip to Main content Skip to Navigation
Conference papers

AN OPEN-SOURCE SPEAKER GENDER DETECTION FRAMEWORK FOR MONITORING GENDER EQUALITY

Abstract : This paper presents an approach based on acoustic analysis to describe gender equality in French audiovisual streams, through the estimation of male and female speaking time. Gender detection systems based on Gaussian Mixture Models , i-vectors and Convolutional Neural Networks (CNN) were trained using an internal database of 2,284 French speakers and evaluated using REPERE challenge corpus. The CNN system obtained the best performance with a frame-level gender detection F-measure of 96.52 and a hourly gender speaking time percentage error bellow 0.6%. It was considered reliable enough to realize large-scale gender equality descriptions. The proposed gender detection system has been packaged as an open-source framework.
Complete list of metadatas

Cited literature [28 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01927560
Contributor : Anthony Larcher <>
Submitted on : Monday, November 19, 2018 - 10:57:53 PM
Last modification on : Monday, February 10, 2020 - 6:14:08 PM
Document(s) archivé(s) le : Wednesday, February 20, 2019 - 4:23:33 PM

File

ddoukhan_icassp_2018.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01927560, version 1

Citation

David Doukhan, Jean Carrive, Félicien Vallet, Anthony Larcher, Sylvain Meignier. AN OPEN-SOURCE SPEAKER GENDER DETECTION FRAMEWORK FOR MONITORING GENDER EQUALITY. IEEE International Conference on Acoustic Speech and Signal Processing, Apr 2018, Calgary, Canada. ⟨hal-01927560⟩

Share

Metrics

Record views

123

Files downloads

1022