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conferenceObject info:eu-repo/semantics/acceptedVersion © IEEE All rights reserved ABSTRACT It is well-known that for speaker recognition task, genderdependent acoustic modeling performs better than genderindependent modeling. The practice is to use the gender ground-truth and to train gender-dependent models. However, such information is not necessarily available, especially if speakers are remotely enrolled. A way to overcome this is to use a gender classification system, which introduces andoi:10.1109/icassp.2017.7953180 dblp:conf/icassp/KanervistoVSHK17 fatcat:kkbjfckvirff7labbi3wyzm66u