A penalized logistic regression approach to detection based phone classification

Recently, we have proposed a detection-based speech recognizer which has two main components: a bank of phonetic feature detectors implemented with hidden Markov models (HMMs), and an event merger. Each detector generates a score that pertains to some phonetic features, e.g. voicing. The merger combines all these scores to generate phone labels. The parameters of the detectors and the merger can be optimized either separately or jointly, and we showed that penalized logistic regression machine (PLRM) is a convenient tool for joint optimization. We validated our approach on a rescoring scheme. In this work, we tackle the phone classification problem and show that high level phone accuracy can be achieved without a direct modeling of the phones when PLRM is used. We also show that better results can be obtained by increasing the number of phonetic features, and that our method outperforms phone classifiers trained either by maximum likelihood estimation, or maximum mutual information.