Abstract

We propose a semi-supervised learning method to improve classification performance in scenarios with limited labeled data. We employ adaptation strategies such as entropy-filtering and self-training, and show that our method achieves up to 17.2% relative improvement in UAR for a multi-class problem. We apply our method to two different tasks: speaker clustering for adult-child interactions during autism assessment sessions, and a variation of the language identification task (LID). We show that in both tasks our method improves classification accuracy while using lesser training data than the baseline and demonstrate the robustness of our setup to the degree of adaptation by controlling the threshold on uncertainty of classification.

Date

2018

Authors

Manoj Kumar, Pavlos Papadopoulos, Ruchir Travadi, Daniel Bone, Shrikanth Narayanan

Conference

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Pages

5149-5153

Publisher

IEEE