Keigo Kubo

Narrow Adaptive Regularization of weights for grapheme-to-phoneme conversion

As the speech recognition field proceeds to open domain and multilingual tasks, the need for robust g2p conversion has been increasing. Towards this objective, we propose a new g2p conversion training method based on the Narrow Adaptive Regularization of Weights (NAROW) online learning algorithm. NAROW improves over its predecessor AROW by automatically adjusting hyperparameters to reduce mistake bounds, and ensuring that the learning rate is not updated when features for the input data have already been updated enough. The contribution of this paper is first to extend NAROW to structured learning, and show the inequality to bound the maximum number of errors in structured NAROW. In experiments, our proposed approach significantly improved over MIRA with consistent phoneme error rate reductions of 1.3-3.8% on a variety of dictionaries.