# Towards Adapting NMF Dictionaries Using Total Variability Modeling for Noise-Robust Acoustic Features

## TL;DR

This paper introduces a novel noise-robust acoustic feature extraction method that adapts NMF dictionaries using Total Variability Modeling, without requiring parallel clean-noisy speech data, and demonstrates competitive performance on noisy speech recognition tasks.

## Contribution

The paper presents a new algorithm combining Total Variability Modeling with NMF for utterance-specific noise adaptation, avoiding the need for parallel training data.

## Key findings

- Features perform comparably to baseline features on noisy data.
- Proposed features are robust to unseen noise conditions.
- Method does not require parallel clean-noisy speech corpus.

## 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.