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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
10.5120/ijais2020451905
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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

    @article{10.5120/ijais2020451905,
    	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 = "http://www.ijais.org/archives/volume12/number36/1113-2020451905",
    	doi = "10.5120/ijais2020451905",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"
    }
    

Abstract

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.

Reference

  1. Abdulwahab, A., MohdYusof, S., & Husni, H. (2017). Acoustic comparison of Malaysia and Nigeria English accents. Journal of Telecommunication, Electronic and Computer Engineering, 141-146.
  2. Ahn, E. (2016). A Computational Approach to Foreign Accent Classification.
  3. Andelman, M. (2011). Flow through capacitor basics. Separation and purification technology, 80(2), 262-269.
  4. Angkititrakul, P., & Hansen, J. H. (2006). Advances in phone-based modeling for automatic accent classification. IEEE Transactions on Audio, Speech, and Language Processing, 14(2), 634-646.
  5. Aswathi Sanal, M. N. G. (2017). Accent Recognition for Malayalam Speech Signals. International Journal of Innovative Research in Computer and Communication Engineering.
  6. Bryant, M., Chow, A., & Li, S. (2014). Classification of Accents of English Speakers by Native Language: Stanford University [cited 14 Dec. 2018]. Available from World Wide Web ….
  7. Chionh, K., Song, M., & Yin, Y. (2018). Application of Convolutional Neural Networks in Accent Identification. Project Report, Carnegie Mellon University, Pittsburgh, Pennsylvania.
  8. Ge, Z., Tan, Y., & Ganapathiraju, A. (2015). Accent classification with phonetic vowel representation. Paper presented at the 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR).
  9. Huckvale, M., & Fang, A. C. (2002). Using phonologically-constrained morphological analysis in continuous speech recognition. Computer Speech & Language, 16(2), 165-181.
  10. Hung, P. N., Van Loan, T., & Quang, N. H. (2016). AUTOMATIC IDENTIFICATION OF VIETNAMESE DIALECTS. Journal of Computer Science and Cybernetics, 32(1), 19-30.
  11. Jain, A., Upreti, M., & Jyothi, P. (2018). Improved Accented Speech Recognition Using Accent Embeddings and Multi-task Learning. Paper presented at the Interspeech.
  12. Krishna, G. R., & Krishnan, R. (2014). Native language identification based on english accent. Paper presented at the International Conference on Natural Language Processing (ICON).
  13. Kumar, A. P., Roy, R., Rawat, S., & Sudhakaran, P. (2017). Continuous Telugu Speech Recognition through Combined Feature Extraction by MFCC and DWPD Using HMM based DNN Techniques. International Journal of Pure and Applied Mathematics, 114(11), 187-197.
  14. Kumpf, K., & King, R. W. (1996). Automatic accent classification of foreign accented Australian English speech. Paper presented at the Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP'96.
  15. Kurzekar, P. K., Deshmukh, R. R., Waghmare, V. B., & Shrishrimal, P. P. (2014). Continuous Speech Recognition System: A Review. Asian Journal of Computer Science and Information Technology, 4(6), 62-66.
  16. Levinson, S. E., Ljolje, A., & Miller, L. G. (1990). Continuous speech recognition from a phonetic transcription. Paper presented at the International Conference on Acoustics, Speech, and Signal Processing.
  17. Mannepalli, K., Sastry, P. N., & Suman, M. (2016). MFCC-GMM based accent recognition system for Telugu speech signals. International Journal of Speech Technology, 19(1), 87-93.
  18. Riyaz, S., Bhavani, B. L., & Kumar, S. V. P. Automatic Speaker Recognition System in Urdu using MFCC & HMM. International Journal of Recent Technology and Engineering (IJRTE), 7.
  19. Stanford. (2010). Machine Learrning from csc229.stanford.edu.
  20. Yusnita, M. A., Paulraj, M., Yaacob, S., & Shahriman, A. B. (2012). Classification of speaker accent using hybrid DWT-LPC features and K-nearest neighbors in ethnically diverse Malaysian English. Paper presented at the 2012 International Symposium on Computer Applications and Industrial Electronics (ISCAIE).

Keywords

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