Latent Prosody Model-Assisted Mandarin Accent Identification

A two-stage latent prosody model-language model (LPM-LM)-based approach is proposed to identify two Mandarin accent types spoken by native speakers in Mainland China and Taiwan. The frontend LPM tokenizes and jointly models the affections of speaker, tone and prosody state of an utterance. The backend LM takes the decoded prosody state sequences and builds n-grams to model the prosodic differences of the two accent types. Experimental results on a mixed TRSC and MAT database showed that fusion of the proposed LPM-LM with a SDC/GMM+PPR-LM+UPR-LM baseline system could further reduced the average accent identification error rate from 20.7% to 16.2%. Therefore, the proposed LPM-LM method is a promising approach.