DPF-based Japanese Phoneme Recognition using Tandem MLNs

Abstract

This paper presents a method for automatic phoneme recognition for Japanese language using tandem MLNs. The method comprises three stages: (i) multilayer neural network (MLN) that converts acoustic features into distinctive phonetic features DPFs, (ii) MLN that combines DPFs and acoustic features as input and generates a 45 dimensional DPF vector with less context effect and (iii) the 45 dimensional feature vector generated by the second MLN are inserted into a hidden Markov model (HMM) based classifier to obtain more accurate phoneme strings from the input speech. From the experiments on Japanese Newspaper Article Sentences (JNAS), it is observed that the proposed method provides a higher phoneme correct rate and improves phoneme accuracy tremendously over the method based on a single MLN. Moreover, it requires fewer mixture components in HMMs.