Speaker Verification Experiments for Adults and Children Using Shared Embedding Spaces
Tuomas Kaseva, Hemant Kumar Kathania, Aku Rouhe, Mikko Kurimo · Aaltodoc (Aalto University) · 2021
In this work, we present our efforts towards developing a robust speaker verification system for children when the data is limited.We propose a novel deep learning -based speaker verification system that combines long-short term memory cells with NetVLAD and additive margin softmax loss.First we investigated these methods on a large corpus of adult data and then applied the best configuration for child speaker verification.For children, the system trained on a large corpus of adult speakers performed worse than a system trained on a much smaller corpus of children's speech.This is due to the acoustic mismatch between training and testing data.To capture more acoustic variability we trained a shared system with mixed data from adults and children.The shared system yields the best EER for children with no degradation for adults.Thus, the single system trained with mixed data is applicable for speaker verification for both adults and children.