Inhibition/Enhancement Network Based ASR using Multiple DPF Extractors

Abstract

This paper describes an evaluation ofInhibition/Enhancement (In/En) network for robustautomatic speech recognition (ASR). In distinctive phoneticfeatures (DPFs) based speech recognition using neuralnetwork, In/En network is needed to discriminate whetherthe DPFs dynamic patterns of trajectories are convex orconcave. The network is used to achieve categorical DPFsmovement by enhancing DPFs peak patterns (convexpatterns) and inhibiting DPFs dip patterns (concavepatterns). We have analyzed the effectiveness of In/Enalgorithm by incorporating it into a system which consists ofthree stages: a) Multilayer Neural Networks (MLNs), b)In/En Network and c) Gram-Schmidt (GS)orthogonalization. From the experiments using JapaneseNewspaper Article Sentences (JNAS) database in clean andnoisy acoustic environments, it is observed that the In/Ennetwork plays a significant role on the improvement ofphoneme recognition performance. Moreover, In/Ennetwork reduces required number of mixture componentsin Hidden Markov Models (HMMs)

Fast Approximate Matching Algorithm for Phone-based Keyword Spotting

“…In the recent decade, techniques for speech processing develop rapidly for many aspects of application, e.g. speech recognition [1,2], language recognition [3,4], speaker verification [5,6], speech blind separation [7,8], etc. As the internet and the computer are widely applied into our daily life, the large amount of audio data can be available easily on the web.…”