Current automatic speech recognition (ASR) systems trained on native speech often perform poorly when applied to non-native or accented speech. In this work, we propose to compute x-vector-like accent embeddings and use them as auxiliary inputs to an acoustic model trained on native data only in order to improve the recognition of multi-accent data comprising native, non-native, and accented speech. In addition, we leverage untranscribed accented training data by means of semi-supervised learning. Our experiments show that acoustic models trained with the proposed accent embeddings outperform those trained with conventional i-vector or x-vector speaker embeddings, and achieve a 15% relative word error rate (WER) reduction on non-native and accented speech w.r.t. acoustic models trained with regular spectral features only. Semi-supervised training using just 1 hour of untranscribed speech per accent yields an additional 15% relative WER reduction w.r.t. models trained on native data only.
Cite as: Turan, M.A.T., Vincent, E., Jouvet, D. (2020) Achieving Multi-Accent ASR via Unsupervised Acoustic Model Adaptation. Proc. Interspeech 2020, 1286-1290, doi: 10.21437/Interspeech.2020-2742
@inproceedings{turan20_interspeech, author={M.A. Tuğtekin Turan and Emmanuel Vincent and Denis Jouvet}, title={{Achieving Multi-Accent ASR via Unsupervised Acoustic Model Adaptation}}, year=2020, booktitle={Proc. Interspeech 2020}, pages={1286--1290}, doi={10.21437/Interspeech.2020-2742} }