@conference{Gemmeke_CHiME_2013,
author = "Jort Gemmeke and Tuomas Virtanen and Antti Hurmalainen",
abstract = "In this work we extend a previously proposed NMF-based technique for speech enhancement of noisy speech to exploit a Hidden Markov Model (HMM). The NMF-based technique works by finding a sparse representation of specrogram segments of noisy speech in a dictionary containing both speech and noise exemplars, and uses the activated dictionary atoms to create a time-varying filter to enhance the noisy speech. In order to take into account larger temporal context and constrain the representation by the grammar of a speech recognizer, we propose to regularize the optimization problem by additionally minimizing the distance between state emission probabilities derived from the speech exemplar activations, and a posteriori state probabilities derived by applying the Forward-Backward algorithm to the emission probabilities. Experiments on Track 1 of the 2nd CHiME Challenge, which contains small vocabulary speech corrupted by both reverberation and authentic living room noise at varying SNRs ranging from 9 to -6 dB, confirm the validity of the proposed technique.",
booktitle = "Proceedings of the 2nd CHiME workshop",
keywords = "speech enhancement;exemplar-based;noise robustness;Non-Negative Matrix Factorization;Hidden Markov Models",
month = "June",
pages = "47-52",
title = "{HMM}-{R}egularization for {NMF}-{B}ased {N}oise {R}obust {ASR}",
url = "https://spandh.dcs.shef.ac.uk/chime_workshop/papers/pP5_gemmeke.pdf",
year = "2013",
}