Non-negative matrix factorization as noise-robust feature extractor for speech recognition

We introduce a novel approach for noise-robust feature extraction in speech recognition, based on non-negative matrix factorization (NMF). While NMF has previously been used for speech denoising and speaker separation, we directly extract time-varying features from the NMF output. To this end we extend basic unsupervised NMF to a hybrid supervised/unsupervised algorithm. We present a Dynamic Bayesian Network (DBN) architecture that can exploit these features in a Tandem manner together with the maximum likelihood phoneme estimate of a bidirectional long short-term memory (BLSTM) recurrent neural network. We show that addition of NMF features to spelling recognition systems can increase word accuracy by up to 7% absolute in a noisy car environment.