Create README.md

README.md

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+HIYACCENT: An Improved Nigerian-Accented Speech Recognition System Based on Contrastive Learning
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+The global objective of this research was to develop a more robust model for the Nigerian English Speakers whose English pronunciations are heavily affected by their mother tongue. The developed model is then compared to the performance of the state of the art models. The project was motivated by the poor performance of existing models on Nigerian Accented English (NAE) Speakers.
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+The Wav2Vec-HIYACCENT model was proposed which introduced a new layer to the Novel Facebook Wav2vec to capture the disparity between the baseline model and NAE. A CTC loss was also inserted on top of the model which adds flexibility to the speech-text alignment. This resulted in over 20% improvement in the performance for NAE.
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