Towards Adapting NMF Dictionaries Using Total Variability Modeling for Noise-Robust Acoustic Features
Abstract
We propose an algorithm to extract noise-robust acoustic features from noisy speech. We use Total Variability Modeling in combination with Non-negative Matrix Factorization (NMF) to learn a total variability subspace and adapt NMF dictionaries for each utterance. Unlike several other approaches for extracting noise-robust features, our algorithm does not require a training corpus of parallel clean and noisy speech. Furthermore, the proposed features are produced by an utterance-specific transform, allowing the features to be robust to the noise occurring in each utterance. Preliminary results on the Aurora 4 + DEMAND noise corpus show that our proposed features perform comparably to baseline acoustic features, including features calculated from a convolutive NMF (CNMF) model. Moreover, on unseen noises, our proposed features gives the most similar word error rate to clean speech compared to the baseline features.
Cite
@article{arxiv.1907.06859,
title = {Towards Adapting NMF Dictionaries Using Total Variability Modeling for Noise-Robust Acoustic Features},
author = {Kunal Dhawan and Colin Vaz and Ruchir Travadi and Shrikanth Narayanan},
journal= {arXiv preprint arXiv:1907.06859},
year = {2019}
}