Achieving Multi-Accent ASR via Unsupervised Acoustic Model Adaptation

Mehmet Ali Tuğtekin Turan (1) , Emmanuel Vincent (1) , Denis Jouvet (1)

1 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication

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.

Mots clés: ASR; accent embedding; speaker adaptation

Domaines: Informatique et langage [cs.CL]; Apprentissage [cs.LG]

Mehmet Ali Tuğtekin Turan, Emmanuel Vincent, Denis Jouvet. Achieving Multi-Accent ASR via Unsupervised Acoustic Model Adaptation. INTERSPEECH 2020, Oct 2020, Shanghai, China. ⟨hal-02907929⟩