%0 Generic %1 li2016collaborative %A Li, Lantian %A Tang, Zhiyuan %A Wang, Dong %A Feng, Yang %A Zhang, Shiyue %D 2016 %K tag %T Collaborative Learning for Language and Speaker Recognition %U http://arxiv.org/abs/1609.08442 %X 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.

@misc{li2016collaborative, 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.}, added-at = {2017-03-12T17:04:26.000+0100}, author = {Li, Lantian and Tang, Zhiyuan and Wang, Dong and Feng, Yang and Zhang, Shiyue}, biburl = {https://www.bibsonomy.org/bibtex/2fbe413e2379b48efe7f30265e8a53772/arianepatrizia}, description = {Collaborative Learning for Language and Speaker Recognition}, interhash = {18b0d6ee66d5a3f6dd71935c6eae838a}, intrahash = {fbe413e2379b48efe7f30265e8a53772}, keywords = {tag}, note = {cite arxiv:1609.08442Comment: Submitted to ICASSP 2017}, timestamp = {2017-03-12T17:04:26.000+0100}, title = {Collaborative Learning for Language and Speaker Recognition}, url = {http://arxiv.org/abs/1609.08442}, year = 2016 }