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									                         ‫مرفق1 : نموذج للملخصات البحثية إنجليزي‬

Title          Phoneme recognition based on distinctive phonetic features
               (DPFs) incorporating a syllable based language model
Author-s       Huda, M.N.; Banik, M.; Muhammad, Ghulam; Kroger, B.J.;
Contact lnfo   ghulam@KSU.EDU.SA
Department     Computer Engineering
citation       12th International Conference on Computers and Information Technology, 2009.
               ICCIT '09, pp: 285 - 289
Year of        2009
Publisher      IEEE Xplore
Type of        Conference paper

URI/DOI        DOI: 10.1109/ICCIT.2009.5407123
Full Text      No
Key words      distinctive phonetic features, local features, multilayer neural networks, Inhibition/
               Enhancement network, hidden Markov models
Abstract       This paper presents a phoneme recognition method based on distinctive phonetic
               features (DPFs). The method comprises three stages. The first stage extracts 3 DPF
               vectors of 15 dimensions each from local features (LFs) of an input speech signal
               using three multilayer neural networks (MLNs). The second stage incorporates an
               Inhibition/Enhancement (In/En) network to obtain more categorical DPF
               movement and decorrelates the DPF vectors using the Gram-Schmidt
               orthogonalization procedure. Then, the third stage embeds acoustic models (AMs)
               and language models (LMs) of syllable-based subwords to output more precise
               phoneme strings. The proposed method provides a higher phoneme correct rate
               as well as phoneme accuracy with fewer mixture components in hidden Markov
               models (HMMs).


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