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Multi-Tier Accent Classification For Improved Transcribing

Corporate companies are becoming more aware of gathering public sentiment, which is facilitated with the presence and vast usage of social networks and media platforms. This is a big data problem, and thus automated machine learning systems are deployed. The process requires the analysis of textual mentions, visual illustrations of the brand and/or respective location, as well as audio mentions of the corporate identity and respective products. When focusing on gathering sentiment analysis from the spoken language, the problem of accent recognition is evident across native and non-native English speakers. Thus, in this research, we investigate the key features of accent recognition, calibrate a proposed system based on previous research using the Wildcat Corpus, and apply on a recent dataset, the Common Voice. Finally applying to a custom dataset gathered from an online media platform. We propose a novel hierarchical classifier solution, trained on the Common Voice dataset and tested on the custom dataset. Our three-tier solution achieved 86% and 89% in the first two levels of accents, and 59% at the final level. From this research, we highlight the issues around the considered datasets and propose a number of recommendations for future researchers. In this research we are not improving or comparing any existing works, but rather offer new insights on the Common Voice dataset. We are presenting a hierarchical classifier for the accent classification problem as proposed.