Analysis and Mitigation of Vocal Effort Variations in Speaker Recognition
Mahesh Kumar Nandwana, Mitchell McLaren, Luciana Ferrer, Diego Castán, Aaron Lawson
Abstract
In this work, we assess the impact of vocal effort on discrimination and calibration performance of a state-of-the-art speaker recognition system. We analyze three levels of vocal effort (low, normal, and high) from the SRI-FRTIV corpus. We use a deep neural network (DNN) speaker embeddings system with probabilistic linear discriminant analysis (PLDA) and find that vocal effort variation significantly degrades system performance. We apply both mixture PLDA (mix-PLDA) and trial-based calibration with condition PLDA similarity (TBC-CPLDA) to improve system robustness. Our proposed approaches resulted in 18% and 33% relative improvement in discrimination and calibration performance respectively on the SRI-FRTIV corpus.
BibTeX
@inproceedings{icassp2019_analysisandmitig,
title = {Analysis and Mitigation of Vocal Effort Variations in Speaker Recognition},
author = {Mahesh Kumar Nandwana and Mitchell McLaren and Luciana Ferrer and Diego Castán and Aaron Lawson},
booktitle = {ICASSP 2019},
year = {2019}
}