Jeffreys divergence-based regularization of neural network output distribution applied to speaker recognition
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2023)
Abstract
A new loss function for speaker recognition with deep neural network is
proposed, based on Jeffreys Divergence. Adding this divergence to the
cross-entropy loss function allows to maximize the target value of the output
distribution while smoothing the non-target values. This objective function
provides highly discriminative features. Beyond this effect, we propose a
theoretical justification of its effectiveness and try to understand how this
loss function affects the model, in particular the impact on dataset types
(i.e. in-domain or out-of-domain w.r.t the training corpus). Our experiments
show that Jeffreys loss consistently outperforms the state-of-the-art for
speaker recognition, especially on out-of-domain data, and helps limit false
alarms.
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Key words
Speaker recognition,deep learning,loss function
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