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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/14953

Title: Bangla phoneme recognition using hybrid features
Authors: Mohammed Rokibul Alam Kotwal
Md. Shahadat Hossain
Issue Date: 2010
Publisher: IEEE xplore
Abstract: This paper presents a Bangla phoneme recognition method for Automatic Speech Recognition (ASR). The method consists of three stages: i) a multilayer neural network (MLN), which converts acoustic features, mel frequency cepstral coefficients (MFCCs), into phoneme probabilities, ii) the phoneme probabilities obtained from the first stage and corresponding Δ and ΔΔ are inserted into another MLN to improve the phoneme probabilities by reducing the context effect and (iii) the phoneme probabilities of current frame and corresponding MFCCs are fed into a hidden Markov model (HMM) based classifier to obtain more accurate phoneme strings. From the experiments on Bangla speech corpus prepared by us, it is observed that the proposed method provides higher phoneme recognition performance than the existing method. Moreover, it requires a fewer mixture components in the HMMs.
URI: http://hdl.handle.net/123456789/14953
Appears in Collections:College of Computer and Information Sciences

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