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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 Major citation 12th International Conference on Computers and Information Technology, 2009. ICCIT '09, pp: 285 - 289 Year of 2009 Publication Publisher IEEE Xplore Sponsor Type of Conference paper Publication ISSN URI/DOI DOI: 10.1109/ICCIT.2009.5407123 Full Text No (Yes,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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