Abstract
Accent identification and classification pose a major challenge for speech recognition systems, as various pronunciations of the same words by speakers of different races are recognized differently by speech recognition systems. Similarly, in most cases, it is difficult for native speakers of the same dialect to understand each other perfectly, especially, if one or more of the speakers has a thick accent. This paper therefore investigates the most accent sensitive words of the three major Nigerian indigenous languages and in addition uses machine learning (ML) to solve the problem of accent classification (AC) of the three languages. A speech-based algorithm was designed and implemented with Python. Speech data were acquired from 300 speakers and mel-frequency cepstral coefficient (MFCC) was employed to extract distinct features which are used to distinguish speakers of the three native languages. The acquired speech data were used to train a combination of a one-dimensional convolutional neural network (1D CNN) and a long short-term memory (LSTM) network model (1D CNN LSTM). Experimental results show a classification accuracy of 94.9%.
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Salau, A.O., Olowoyo, T.D., Akinola, S.O. (2020). Accent Classification of the Three Major Nigerian Indigenous Languages Using 1D CNN LSTM Network Model. In: Jain, S., Sood, M., Paul, S. (eds) Advances in Computational Intelligence Techniques. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-2620-6_1
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