This paper presents a technique that exploits the denoised speech\u2019s variance, estimated during the speech feature enhancement process, to improve noise-robust speech recognition. This technique provides an alternative to the Bayesian predictive classification decision rule by carrying out an integration over the feature space instead of over the model-parameter space, offering a much simpler system implementation and lower computational cost. We extend our earlier work by using a new approach, based on a parametric model of speech distortion and thus free from the use of any stereo training data, to statistical feature enhancement, for which a novel algorithm for estimating the variance of the enhanced speech features is developed. Experimental evaluation using the full Aurora2 test data sets demonstrates an 11.4% digit error rate reduction averaged over all noisy and SNR conditions, compared with the best technique we have developed [2] prior to this work that did not exploit the variance information and that required no stereo training data.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper presents a technique that exploits the denoised speech\u2019s variance, estimated during the speech feature enhancement process, to improve noise-robust speech recognition. This technique provides an alternative to the Bayesian predictive classification decision rule by carrying out an integration over the feature space instead of over the model-parameter space, offering a much simpler system […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"deng","user_id":"31602"},{"type":"user_nicename","value":"jdroppo","user_id":"32211"},{"type":"user_nicename","value":"alexac","user_id":"30932"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proc. 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