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Mask estimation and imputation methods for missing data speech recognition in a multisource reverberant environment
2013
Computer Speech and Language
We present an automatic speech recognition system that uses a missing data approach to compensate for challenging environmental noise containing both additive and convolutive components. The unreliable and noisecorrupted ("missing") components are identified using a Gaussian mixture model (GMM) classifier based on a diverse range of acoustic features. To perform speech recognition using the partially observed data, the missing components are substituted with clean speech estimates computed
doi:10.1016/j.csl.2012.06.005
fatcat:x7uuskic5bgqzkj4ixoml6u27e