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Register a new userDiscussion on domain generalization in the cross-device speaker verification system
مناقشة حول تعميم المجال في نظام التحقق من مكبرات الصوت
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في هذه الورقة، نستخدم تعميم المجال لتحسين أداء نظام التحقق من مكبر الصوت عبر الأجهزة.استنادا إلى نظام التحقق من المتكلم التدريبي، نستخدم خوارزميات تعميم المجال لضبط المعلمات النموذجية.أولا، نستخدم DataSet Voxceleb2 لتدريب ECAPA-TDNN كنموذج أساسي.ثم استخدم مجموعة بيانات ChT-TDSV وخوارزميات تعميم المجال التالية لضبطها: Dann، CDNN، Coral Coral.اختبارات نظامنا المقترح 10 سيناريوهات مختلفة في مجموعة بيانات NSYSU-TDSV، بما في ذلك جهاز واحد وأجهزة متعددة.أخيرا، في سيناريو الأجهزة المتعددة، انخفض أفضل معدل خطأ على قدم المساواة من 18.39 في الأساس إلى 8.84.حقق بنجاح تحديد الهوية عبر الجهاز على نظام التحقق من مكبر الصوت.
In this paper, we use domain generalization to improve the performance of the cross-device speaker verification system. Based on a trainable speaker verification system, we use domain generalization algorithms to fine-tune the model parameters. First, we use the VoxCeleb2 dataset to train ECAPA-TDNN as a baseline model. Then, use the CHT-TDSV dataset and the following domain generalization algorithms to fine-tune it: DANN, CDNN, Deep CORAL. Our proposed system tests 10 different scenarios in the NSYSU-TDSV dataset, including a single device and multiple devices. Finally, in the scenario of multiple devices, the best equal error rate decreased from 18.39 in the baseline to 8.84. Successfully achieved cross-device identification on the speaker verification system.
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