A TRANSFER LEARNING APPROACH FOR PRONUNCIATION SCORING

Phone-level pronunciation scoring is a very challenging task, with performance far from that of human annotators. Standard systems generate a score for each phone in a phrase using models trained for automatic speech recognition (ASR) with native data only. Better performance has been shown when using systems that are trained specifically for the task using non-native data. Yet, such systems face the challenge that datasets labelled for this task are scarce and usually small. In this paper, we present a transfer learning-based approach that leverages a model trained for ASR, adapting it for the task of pronunciation scoring. We analyze the effect of several hyperparameters and design choices and compare the performance with a state-of-the-art goodness of pronunciation (GOP) system. Our final model is, on average, 20% better than the GOP model on a cost function that prioritizes low rates of unnecessary corrections.