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Improving Semi-Supervised Classification for Low-Resource Speech Interaction Applications

Citation Author(s):
Pavlos Papadopoulos, Ruchir Travadi, Daniel Bone, Shrikanth Narayanan
Submitted by:
Manoj Kumar
Last updated:
12 April 2018 - 6:08pm
Document Type:
Poster
Document Year:
2018
Event:
Presenters:
Manoj Kumar
 

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.

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