Discriminative segmental models, such as segmental conditional random fields (SCRFs) and segmental structured support vector machines (SSVMs), have had success in speech recognition via both lattice rescoring and first-pass decoding. However, such models suffer from slow decoding, hampering the use of computationally expensive features, such as segment neural networks or other high-order features. A typical solution is to use approximate decoding, either by beam pruning in a single pass or by beam pruning to generate a lattice followed by a second pass. In this work, we study discriminative segmental models trained with a hinge loss (i.e., segmental structured SVMs). We show that beam search is not suitable for learning rescoring models in this approach, though it gives good approximate decoding performance when the model is already well-trained. Instead, we consider an approach inspired by structured prediction cascades, which use max-marginal pruning to generate lattices. We obtain a highaccuracy phonetic recognition system with several expensive feature types: a segment neural network, a second-order language model, and second-order phone boundary features. Index Termssegmental conditional random field, structured prediction cascades, phone recognition, segment neural network, beam search
We compare this approach to an efficient first-pass segmental model REF .
Discriminative segmental cascades for feature-rich phone recognition
2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)
2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)
Computer Science