Further Experiments with Detector-Based Conditional Random Fields in Phonetic Recognition

In our prior work with conditional random fields (CRFs), we have shown that it is possible to achieve results in the phonetic recognition task with a CRF that approach the results of a similarly trained HMM system (but with many fewer parameters), and we have shown that using two different feature sets that are supposedly redundant gives an improvement in the performance of the CRF. In this paper, we explore two new areas with our CRF model. First, we show that by using two feature sets that are just transforms of each other, we achieve an improvement of results in the CRF model. Second, we show that by adding a single pass of realignment to our CRF model training, we achieve an accuracy result in the phone recognition task that is superior to that of an HMM system trained with triphone labels, despite only training the CRF on monophone labels with no explicit triphonic context.