Effect of articulatory Δ and ΔΔ parameters on multilayer neural network based speech recognition

This paper describes an effect of articulatory dynamic parameters (Δ and ΔΔ) on neural network based automatic speech recognition(ASR). Articulatory features (AFs) or distinctive phonetic features (DPFs)-based system shows its superiority in performances over acoustic features- based in 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 three stages: (i) DPFs extraction using a multilayer neural network (MLN) from acoustic features, (ii) incorporation of dynamic parameters into another MLN for reducing DPF context, and (iii) addition of an Inhibition/Enhancement (In/En) network for categorizing the DPF movement more accurately and Gram-Schmidt (GS) orthogonalization procedure for decorrelating the inhibited/enhanced data vector before connecting with hidden Markov model (HMMs)-based classifier. From the experiments on Japanese Newspaper Article Sentences (JNAS), it is observed that the proposed method provides a higher phoneme correct rate over the method that does not incorporate dynamic articulatory parameters. Moreover, it reduces mixture components in HMM for obtaining a higher recognition performance.