An Inhibition/Enhancement network for noise robust ASR

Inhibition/Enhancement Network Based ASR using Multiple DPF Extractors

“…For the phoneme recognizer using two MLNs [5], PCRs for D5 and D6 data set are evaluated using an HMM-based classifier. The D1 data set is used to design 38 Japanese monophone HMMs with five states, three loops, and left-to-right models.…”

“…The D4 data set is used to design 38 Japanese monophone HMMs for the extended proposed method and the existing methods, (a), (b), (c), (d), (e), (f) and (g). The same constructions of HMMs like as two MLNs [5] are used in the proposed method.…”

“…On the other hand, we analyzed the effectiveness of In/En algorithm [5] in practical conditions by incorporating it into a system which consists of three stages: a) Multilayer Neural Networks (MLNs) to extract DPFs from acoustic features, local features (LFs) [6], b) In/En Network and c) Gram-Schmidt (GS) orthogonalization.…”

“…In this study, we propose a DPF extraction method for constructing a more accurate phoneme recognizer. This method incorporates (i) three MLNs instead of two MLNs [5], first MLN is same as [5] and the other two MLNs are: MLN cntxt , which reduces phoneme boundary errors, and MLN Dyn , which restricts DPF dynamics, and then embeds (ii) an In/En network to obtain more precise DPF patterns for an HMM-based classifier by enhancing convex patterns (DPF peaks) and by inhibiting concave patterns (DPF dips). The effects of three MLNs and the In/En network in our method increase phoneme recognition performance significantly over the other investigated method.…”

“…In this study, we investigate and evaluate three types of DPF extraction methods along with the conventional MFCC-based method from the viewpoint of phoneme recognition performance. These methods are (i) DPF using an MLN, (ii) DPF using two MLNs [5] and (iii) our proposed method using three MLNs and an In/En network.…”