In many scenarios of a language identification task, the user will specify a small set of languages which he/she can speak instead of a large set of all possible languages. We want to model such prior knowledge into the way we train our neural networks, by replacing the commonly used softmax loss function with a novel loss function named tuplemax loss. As a matter of fact, a typical language identification system launched in North America has about 95% users who could speak no more than two languages. Using the tuplemax loss, our system achieved a 2.33% error rate, which is a relative 39.4% improvement over the 3.85% error rate of standard softmax loss method.
Cite
@article{arxiv.1811.12290,
title = {Tuplemax Loss for Language Identification},
author = {Li Wan and Prashant Sridhar and Yang Yu and Quan Wang and Ignacio Lopez Moreno},
journal= {arXiv preprint arXiv:1811.12290},
year = {2019}
}