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Modern Automatic Speech Recognition (ASR) technology has evolved to identifythe speech spoken by native speakers of a language very well. However,identification of the speech spoken by non-native speakers continues to be amajor challenge for it. In this work, we first spell out the key requirementsfor creating a well-curated database of speech samples in non-native accentsfor training and testing robust ASR systems. We then introduce AccentDB, onesuch database that contains samples of 4 Indian-English accents collected byus, and a compilation of samples from 4 native-English, and a metropolitanIndian-English accent. We also present an analysis on separability of thecollected accent data. Further, we present several accent classification modelsand evaluate them thoroughly against human-labelled accent classes. We test thegeneralization of our classifier models in a variety of setups of seen andunseen data. Finally, we introduce the task of accent neutralization ofnon-native accents to native accents using autoencoder models withtask-specific architectures. Thus, our work aims to aid ASR systems at everystage of development with a database for training, classification models forfeature augmentation, and neutralization systems for acoustic transformationsof non-native accents of English.

AccentDB: A Database of Non-Native English Accents to Assist Neural Speech Recognition