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Distinctive Phonetic Feature (DPF) Extraction Based on MLNs and Inhibition/Enhancement Network

Mohammad Nurul HUDA, Hiroaki KAWASHIMA, Tsuneo NITTA

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Summary :

This paper describes a distinctive phonetic feature (DPF) extraction method for use in a phoneme recognition system; our method has a low computation cost. This method comprises three stages. The first stage uses two multilayer neural networks (MLNs): MLNLF-DPF, which maps continuous acoustic features, or local features (LFs), onto discrete DPF features, and MLNDyn, which constrains the DPF context at the phoneme boundaries. The second stage incorporates inhibition/enhancement (In/En) functionalities to discriminate whether the DPF dynamic patterns of trajectories are convex or concave, where convex patterns are enhanced and concave patterns are inhibited. The third stage decorrelates the DPF vectors using the Gram-Schmidt orthogonalization procedure before feeding them into a hidden Markov model (HMM)-based classifier. In an experiment on Japanese Newspaper Article Sentences (JNAS) utterances, the proposed feature extractor, which incorporates two MLNs and an In/En network, was found to provide a higher phoneme correct rate with fewer mixture components in the HMMs.

Publication
IEICE TRANSACTIONS on Information Vol.E92-D No.4 pp.671-680
Publication Date
2009/04/01
Publicized
Online ISSN
1745-1361
DOI
10.1587/transinf.E92.D.671
Type of Manuscript
PAPER
Category
Speech and Hearing

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