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August Edition 2022

International Journal of Applied Information Systems solicits high quality original research papers for the August 2022 Edition of the journal. The last date of research paper submission is July 15, 2022.

Automatic Speech Recognition and Accent Identification of Ethnically Diverse Nigerian English Speakers

Francisca O. Oladipo, Rahmon A. Habeeb, Abraham E. Musa, Chinecherem Umezuruike, Ohieku Andrew Adeiza in Signal Processing

International Journal of Applied Information Systems
Year of Publication:2021
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors:Francisca O. Oladipo, Rahmon A. Habeeb, Abraham E. Musa, Chinecherem Umezuruike, Ohieku Andrew Adeiza
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  1. Francisca O Oladipo, Rahmon A Habeeb, Abraham E Musa, Chinecherem Umezuruike and Ohieku Andrew Adeiza. Automatic Speech Recognition and Accent Identification of Ethnically Diverse Nigerian English Speakers. International Journal of Applied Information Systems 12(36):41-48, May 2021. URL, DOI BibTeX

    	author = "Francisca O. Oladipo and Rahmon A. Habeeb and Abraham E. Musa and Chinecherem Umezuruike and Ohieku Andrew Adeiza",
    	title = "Automatic Speech Recognition and Accent Identification of Ethnically Diverse Nigerian English Speakers",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "May 2021",
    	volume = 12,
    	number = 36,
    	month = "May",
    	year = 2021,
    	issn = "2249-0868",
    	pages = "41-48",
    	url = "",
    	doi = "10.5120/ijais2020451905",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"


It is imperative to improve the speech recognition system as human-machine interfaces are advancing in the growing global market of technologies. There are quite a number of Nigerian English speakers’ accents to which the speech recognition systems are not sufficiently exposed. Accents may suggest a lot of information about someone’s whereabouts, for example, their native language, place of origin, or ethnic groups and accent classification. Given the importance of accents, efficiency and accuracy of speech recognition systems can be improved with training data of diverse accents. This research provides support for accent-dependent automatic speech recognition by deploying a supervised learning algorithm to the task of recognizing three Nigerian ethnic groups (Yoruba, Igbo, and Hausa) and distinguish them based on their accents by constructing sequential Mel-Frequency Cepstral Coefficients (MFCC) features from the frames of the audio sample. Our results show that concatenating the MFCC features sequentially and applying a supervised learning technique to provide a solution to the problem of identifying and classifying accents works efficiently and accurately.


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Acoustic modeling, non-native speaker, speech recognition, supervised learning