--- res: bibo_abstract: - In this contribution we extend a previously proposed Bayesian approach for the enhancement of reverberant logarithmic mel power spectral coefficients for robust automatic speech recognition to the additional compensation of background noise. A recently proposed observation model is employed whose time-variant observation error statistics are obtained as a side product of the inference of the a posteriori probability density function of the clean speech feature vectors. Further a reduction of the computational effort and the memory requirements are achieved by using a recursive formulation of the observation model. The performance of the proposed algorithms is first experimentally studied on a connected digits recognition task with artificially created noisy reverberant data. It is shown that the use of the time-variant observation error model leads to a significant error rate reduction at low signal-to-noise ratios compared to a time-invariant model. Further experiments were conducted on a 5000 word task recorded in a reverberant and noisy environment. A significant word error rate reduction was obtained demonstrating the effectiveness of the approach on real-world data.@eng bibo_authorlist: - foaf_Person: foaf_givenName: Volker foaf_name: Leutnant, Volker foaf_surname: Leutnant - foaf_Person: foaf_givenName: Alexander foaf_name: Krueger, Alexander foaf_surname: Krueger - foaf_Person: foaf_givenName: Reinhold foaf_name: Haeb-Umbach, Reinhold foaf_surname: Haeb-Umbach foaf_workInfoHomepage: http://www.librecat.org/personId=242 bibo_doi: 10.1109/TASL.2013.2258013 bibo_issue: '8' bibo_volume: 21 dct_date: 2013^xs_gYear dct_language: eng dct_subject: - Bayes methods - compensation - error statistics - reverberation - speech recognition - Bayesian feature enhancement - background noise - clean speech feature vectors - compensation - connected digits recognition task - error statistics - memory requirements - noisy reverberant data - posteriori probability density function - recursive formulation - reverberant logarithmic mel power spectral coefficients - robust automatic speech recognition - signal-to-noise ratios - time-variant observation - word error rate reduction - Robust automatic speech recognition - model-based Bayesian feature enhancement - observation model for reverberant and noisy speech - recursive observation model dct_title: Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition@ ...