Connected Letter Recognition with a Multi-state Time Delay Neural Network

Abstract

The Multi-State Time Delay Neural Network (MS-TDNN) integrates a nonlinear time alignment procedure (DTW) and the high-accuracy phoneme spotting capabilities of a TDNN into a connectionist speech recognition system with word-level classification and error backpropagation. We present an MS-TDNN for recognizing continuously spelled letters, a task characterized by a small but highly confusable vocabulary. Our MS-TDNN achieves 98.5/92.0% word accuracy on speaker dependent/independent tasks, outperforming previously reported results on the same databases. We propose training techniques aimed at improving sentence level performance, including free alignment across word boundaries, word duration modeling and error backpropagation on the sentence rather than the word level. Architectures integrating submodules specialized on a subset of speakers achieved further improvements.