Accent Identification of Ethnically Diverse Nigerian English Speakers

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.