Active learning and semi-supervised learning for speech recognition: A unified framework using the global entropy reduction maxi

We propose a unified global entropy reduction maximization (GERM) framework for active learning and semi-supervised learning for speech recognition. Active learning aims to select a limited subset of utterances for transcribing from a large amount of un-transcribed utterances, while semi-supervised learning addresses the problem of selecting right transcriptions for un-transcribed utterances, so that the accuracy of the automatic speech recognition system can be maximized. We show that both the traditional confidence-based active learning and semi-supervised learning approaches can be improved by maximizing the lattice entropy reduction over the whole dataset. We introduce our criterion and framework, show how the criterion can be simplified and approximated, and describe how these approaches can be combined. We demonstrate the effectiveness of our new framework and algorithm with directory assistance data collected under the real usage scenarios and show that our GERM based active le...

• Type: Journal
• Year: 2010
• Where: CSL
• Authors: Dong Yu, Balakrishnan Varadarajan, Li Deng, Alex Acero
• Keywords: Automated Reasoning, CSL 2010, Entropy Reduction, Semi-supervised Learning, Speech Recognition