State-dependent mixture tying with variable codebook size for accented speech recognition

Abstract

In this paper, we propose a state-dependent tied mixture (SDTM) models with variable codebook size to improve the model robustness for accented phonetic variations while maintaining model discriminative ability. State tying and mixture tying are combined to generate SDTM models. Compared to a pure mixture tying system, the SDTM model uses state tying to reserve the state identity; compared to the sole state tying system, such model uses a small set of parameters to discard the overlapping mixture distributions for robust model estimation. The codebook size of SDTM model is varied according to the confusion probability of states. The more confusable a state is, the larger its codebook size gets for a higher degree of model resolution. The codebook size is governed by state level variation probability of accented phonetic confusions which can be automatically extracted by frame-to-state alignment based on the local model mismatch. The effectiveness of this approach is evaluated on Mandarin accented speech. Our method yields a significant 2.1%, 9.5% and 3.5% absolute word error rate reduction compared with state tying, mixture tying and state-based phonetic tied mixtures, respectively.