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dc.contributor.authorMokbel, Chaficen_US
dc.description.abstractThis paper introduces a unified framework for online adaptation of hidden Markov models (HMM) parameters to real-life conditions. Hence, it aims at improving the robustness of speech recognition systems. In addition, it describes some techniques developed to control the convergence of adaptation in unsupervised modes. Classically, two approaches have been used to adapt HMM parameters to new conditions, that is, Bayesian adaptation and spectral transformation-generally using linear regression. This paper lays out a unifying framework where both Bayesian adaptation and spectral transformation adaptation are seen as particular cases. In this sense, the framework attributes one transformation to each Gaussian distribution and partitions the latter automatically with respect to the adaptation data. Thus, the transformations of each class would share the same parameter vector. Consequently, the global transformation gets a data-driven freedom degree. The parameters of the global transformation are determined according to the maximum a posteriori (MAP) criterion using the original HMM a priori distributions. The general adaptation algorithm has been implemented within the CNET speech recognition system and the whole system evaluated on several field-telephone databases. The new adaptation method provides us with a systematic convergence in an online unsupervised mode of the speech recognition system toward a system enrolled with field data in a supervised mode.en_US
dc.format.extent15 p.en_US
dc.subjectHidden Markov modelsen_US
dc.subjectBayesian methodsen_US
dc.subjectSpeech recognitionen_US
dc.subjectMaximum likelihood estimationen_US
dc.subjectLinear regressionen_US
dc.subject.lcshGaussian distributionen_US
dc.subject.lcshAutomatic speech recognitionen_US
dc.titleOnline adaptation of HMMs to real-life conditions: a unified frameworken_US
dc.typeJournal Articleen_US
dc.contributor.affiliationDepartment of Electrical Engineeringen_US
dc.relation.ispartoftextIEEE transactions on speech and audio processingen_US
Appears in Collections:Department of Electrical Engineering
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