Inhibition/Enhancement Network Based ASR using Multiple DPF Extractors

—— This paper describes an evaluation of Inhibition/Enhancement (In/En) network for robust automatic speech recognition (ASR). In distinctive phonetic features (DPFs) based speech recognition using neural network, In/En network is needed to discriminate whether the DPFs dynamic patterns of trajectories are convex or concave. The network is used to achieve categorical DPFs movement by enhancing DPFs peak patterns (convex patterns) and inhibiting DPFs dip patterns (concave patterns). We have analyzed the effectiveness of In/En algorithm by incorporating it into a system which consists of three stages: a) Multilayer Neural Networks (MLNs), b) In/En Network and c) Gram-Schmidt (GS) orthogonalization. From the experiments using Japanese Newspaper Article Sentences (JNAS) database in clean and noisy acoustic environments, it is observed that the In/En network plays a significant role on the improvement of phoneme recognition performance. Moreover, In/En network reduces required number of mixture components in Hidden Markov Models (HMMs). Index Terms—— Articulatory Features, Hidden Markov Model, Inhibition/Enhancement Network, Local Features, Multilayer Neural Network, Distinctive Phonetic Features.