HYBRID DNN-LATENT STRUCTURED SVM ACOUSTIC MODELS FOR CONTINUOUS SPEECH RECOGNITION

In this work, we propose Deep Neural Network (DNN)-Latent Structured Support Vector Machine (LSSVM) Acoustic Models as replacement for more standard sequence-discriminative trained DNN-HMM hybrid acoustic models. Compared to existing methods, approaches based on margin maximization, as is considered in this work, enjoy better theoretical justification. In addition to a max-margin based criteria, we also extend the Structured SVM model to include latent variables in the model to account for uncertainty in state alignments. Introducing latent structure allows for better sample complexity, often requiring 3 3 % to 6 6 % fewer utterances to converge compared to alternate criteria. On an 8-hour independent test set of conversational speech, the proposed method decreases word error rate by 9% relative to a cross-entropy trained hybrid system, while the best existing system decreases the word error rate by 6.5% relative.