Articulatory Δ and ΔΔ parameters effect on HMM-based classifier for ASR

This paper describes an effect of articulatory Δ and ΔΔ parameters on automatic speech recognition (ASR). Articulatory features (AFs) or distinctive phonetic features (DPFs)-based system shows its superiority in performances over acoustic features based ASR. These performances can be further improved by incorporating articulatory dynamic parameters into it. In this paper, we have proposed such a phoneme recognition system that comprises two stages: (i) DPFs extraction using a multilayer neural network (MLN) from acoustic features, local features (LFs) and (ii) incorporation of dynamic parameters (Δ and ΔΔ) into a hidden Markov model (HMM) based classifier for more accurate performances. From the experiments on Japanese Newspaper Article Sentences (JNAS), it is observed that the proposed method provides a higher phoneme correct rate and phoneme accuracy over the method that does not incorporate dynamic articulatory parameters. Moreover, it reduces mixture components in HMM for obtaining a higher performance.