Effects of Syllable Language Model on Distinctive Phonetic Features (DPFs) based Phoneme Recognition Performance

This paper presents a distinctive phonetic features (DPFs) based phoneme recognition method by incorporating syllable language models (LMs). The method comprises three stages. The first stage extracts three DPF vectors of 15 dimensions each from local features (LFs) of an input speech signal using three multilayer neural networks (MLNs). The second stage incorporates an Inhibition/Enhancement (In/En) network to obtain more categorical DPF movement and decorrelates the DPF vectors using the Gram-Schmidt orthogonalization procedure. Then, the third stage embeds acoustic models (AMs) and LMs of syllable-based subwords to output more precise phoneme strings. From the experiments, it is observed that the proposed method provides a higher phoneme correct rate as well as a tremendous improvement of phoneme accuracy. Moreover, it shows higher phoneme recognition performance at fewer mixture components in hidden Markov models (HMMs).