It has become increasingly desirable to learn via the internet in developing countries. Most students seeking to learn online would naturally flock towards Massive Open Online Courses (MOOC). These are largely offered in English, a norm reflected in the use of English in scientificc literature.
While there may be positive e ffects of listening to a foreign accent on listening comprehension in speci fic younger age groups, this is not more generally the case. Limited prior exposure to a given accent often results in reduced listening comprehension.
This challenge naturally reoccurs in the domain of internet-based learning, where listening comprehension is inherently tied to the utility of a spoken lecture or course. Our project aims to provide greater variety of options for internet-based education by creating algorithmic solutions that allow students to learn online in accents that are more familiar to them, if they so desire.
Same content, diff erent accent. We expect we can achieve this by engineering a generative model which can be used to convert audio between various accents. We conceptualise this as a challenge in accent transfer. Website
