Understanding Automatic Speech Recognition for L2 Speakers and Unintended Discrimination in Artificial Intelligence

Abstract: The thesis aimed to investigate the effects of unintended bias in artificial intelligence has on society and if it was possible to improve the performance of Auto-Speech- Recognition models by training them on non-native Swedish speakers. Two Automatic Speech Recognition systems, Microsoft Azure and Google cloud speech-to-text, were used in the process. Re-trained models were created in order to improve their recognition ability. The models were later evaluated by comparing the word error rates for the re-trained models and the pre-trained models. The study found that re-training the model on non-native speakers improved the performance of the Auto-Speech-Recognition models. This study can be of interest for researches concerning data set bias and how it affects the artificial intelligence models performance. It also helps the reader to understand how auto-speech-recognition models and their basic structure works.