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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/14938

Title: Automatic speech recognition for Bangla digits
Authors: Ghulam Muhammad
Yousef Ajami Alotaibi
mohammad nurul huda.
Keywords: Automatic speech recognition, BangIa digit, BangIa phoneme, hidden Markov model
Issue Date: 2006
Publisher: IEEE Xplore
Abstract: In this paper, we introduce a system for Bangla digit automatic speech recognition (ASR). Though Bangla is one of the largely spoken languages in the world, only a few works on Bangla ASR can be found in the literature, especially on Bangladeshi accented Bangla. In this work, the corpus is collected from natives in Bangladesh. Mel-frequency cepstral coefficients (MFCCs) based features and hidden Markov model (HMM) based classifiers are used for recognition. Experimental results show comparatively high recognition performance (more than 95%) for first six digits (0 - 5) and low performance (less than 90%) for the next four digits (6 - 9). We notice two confused pairs of digits: one with (6) and (9), and the other with (7) and (8), in the experiments. We also find that different dialects in Bangladesh have a greater role on this confusion.
URI: http://hdl.handle.net/123456789/14938
Appears in Collections:College of Computer and Information Sciences

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