Data Augmentation Improves Recognition of Foreign Accented Speech

Speech recognition of foreign accented (non-native or L2) speech remains a challenge to the state-of-the-art. The most common approach to address this scenario involves the collection and transcription of accented speech, and incorporating this into the training data. However, the amount of accented data is dwarfed by the amount of material from native (L1) speakers, limiting the impact of the additional material. In this work, we address this problem via data augmentation. We create modified copies of two accents, Latin American and Asian accented English speech with voice transformation (modifying glottal source and vocal tract parameters), noise addition, and speed modification. We investigate both supervised (where transcription of the accented data is available) and unsupervised approaches to using the accented data and associated augmentations. We find that all augmentations provide improvements, with the largest gains coming from speed modification, then voice transformation and noise addition providing the least improvement. The improvements from training accent specific models with the augmented data are substantial. Improvements from supervised and unsupervised adaptation (or training with soft labels) with the augmented data are relatively minor. Overall, we find speed modification to be a remarkably reliable data augmentation technique for improving recognition of foreign accented speech. Our strategies with associated augmentations provide Word Error Rate (WER) reductions of up to 30% relative over a baseline trained with only the accented data.