Collaborative Learning for Language and Speaker Recognition

TL;DR

This paper introduces a multi-task recurrent neural network that jointly performs language and speaker recognition, leveraging collaborative learning to enhance accuracy in both tasks.

Contribution

It proposes a novel unified model where language and speaker recognition tasks are integrated through a multi-task recurrent neural network, enabling mutual improvement.

Findings

Multi-task model outperforms task-specific models on both tasks

Collaborative learning improves recognition accuracy

Joint modeling benefits both language and speaker recognition

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.