Vimal Manohar

Accent-Robust Automatic Speech Recognition Using Supervised and Unsupervised Wav2vec Embeddings

2021 · arXiv (Cornell University)

Speech recognition models often obtain degraded performance when tested on speech with unseen accents. Domain-adversarial training (DAT) and multi-task learning (MTL) are two common approaches for building accent-robust ASR models. ASR models using accent embeddings …

Semi-Supervised Training of Acoustic Models Using Lattice-Free MMI

2018

The lattice-free MMI objective (LF-MMI) has been used in supervised training of state-of-the-art neural network acoustic models for automatic speech recognition (ASR). With large amounts of unsupervised data available, extending this approach to the semi-supervised …