AN OPEN-SOURCE SPEAKER GENDER DETECTION FRAMEWORK FOR MONITORING GENDER EQUALITY
| Autor: | Sylvain Meignier, Jean Carrive, David Doukhan, Anthony Larcher, Félicien Vallet |
|---|---|
| Přispěvatelé: | Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris Saclay (COmUE)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université - UFR d'Ingénierie (UFR 919), Sorbonne Université (SU)-Sorbonne Université (SU)-Université Paris-Saclay-Université Paris-Sud - Paris 11 (UP11), Institut National de l'Audiovisuel (INA), Laboratoire d'Informatique de l'Université du Mans (LIUM), Le Mans Université (UM) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Gender equality
Computer science Speech recognition Speech Gender De-tection Feature extraction 020206 networking & telecommunications Convolutional Neural Network 02 engineering and technology 16. Peace & justice Mixture model Convolutional neural network i-vector [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Digital Humanities Open source 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing [INFO]Computer Science [cs] Hidden Markov model |
| Zdroj: | IEEE International Conference on Acoustic Speech and Signal Processing IEEE International Conference on Acoustic Speech and Signal Processing, Apr 2018, Calgary, Canada ICASSP |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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