Collaborative Learning for Language and Speaker Recognition
Sound
2017-05-24 v2 Computation and Language
Abstract
This paper presents a unified model to perform language and speaker recognition simultaneously and altogether. The model is based on a multi-task recurrent neural network where the output of one task is fed as the input of the other, leading to a collaborative learning framework that can improve both language and speaker recognition by borrowing information from each other. Our experiments demonstrated that the multi-task model outperforms the task-specific models on both tasks.
Cite
@article{arxiv.1609.08442,
title = {Collaborative Learning for Language and Speaker Recognition},
author = {Lantian Li and Zhiyuan Tang and Dong Wang and Andrew Abel and Yang Feng and Shiyue Zhang},
journal= {arXiv preprint arXiv:1609.08442},
year = {2017}
}