In recent years, structured online discriminative learning methods using second order statistics have been shown to outperform conventional generative and discriminative models in the grapheme-to-phoneme (g2p) conversion task. However, these methods update the parameters by sequentially using N-best hypotheses predicted with the current parameters. Thus, the parameters appearing in early hypotheses are overfitted compared with those in later hypotheses. In this paper, we propose a novel method called structured soft margin confidence weighted learning, which extends multi-class confidence weighted learning to structured learning. The proposed method extends multi-class CW in two ways, allowing for improved robustness to overfitting: (1) regularization inspired by soft margin support vector machines, allowing for margin error, and (2) update using N-best hypotheses simultaneously and interdependently. In an evaluation experiment on the g2p conversion task, the proposed method improved over all other approaches in terms of phoneme error rate with a significant difference.
Bibliographic reference. Kubo, Keigo / Sakti, Sakriani / Neubig, Graham / Toda, Tomoki / Nakamura, Satoshi (2014): "Structured soft margin confidence weighted learning for grapheme-to-phoneme conversion", In INTERSPEECH-2014, 1263-1267.