In our previous study, we proposed factored MLLR (FMLLR) where each MLLR parameter is defined as a function of a control vector. We presented a method to train the FMLLR parameters based on a general framework of the expectationmaximization (EM) algorithm. In this paper, we extend the FMLLR structure from diagonal to unrestricted full matrix with a sophisticated algorithm for the training of re...

In this paper, regression-tree based spectral peak alignment is proposed for rapid speaker adaptation using the linearization of VTLN. Two different regression classes are investigated: phonetic classes (using combined knowledge and data-driven techniques) and mixture classes. Compared to MLLR and VTLN, improved performance can be obtained for both supervised and unsupervised adaptations on bot...

Discounted Likelihood Linear Regression (DLLR) is a speaker adaptation technique for cases where there is insufficient data for MLLR adaptation. Here, we provide an alternative derivation of DLLR by using a censored EM formulation which postulates additional adaptation data which is hidden. This derivation shows that DLLR, if allowed to converge, provides maximum likelihood solutions. Thus the ...

This paper proposes a speaker adaptation technique using a nonlinear spectral transform based on GMMs. One of the most popular forms of speaker adaptation is based on linear transforms, e.g., MLLR. Although MLLR uses multiple transforms according to regression classes, only a single linear transform is applied to each state. The proposed method performs nonlinear speaker adaptation based on a n...

Hidden Markov Models HMMs have been used with consider able success in continuous speech recognition It is well known that high accuracy can be obtained when the HMM system is trained and tested in a quiet environment and the speech signal is acquired from a close talking microphone However mismatches between training and testing environment severely degrade erformance Two major sources of mism...

Performance of automatic speech recognition systems trained on close talking data su ers when used in a distant talking environment due to the mismatch in training and testing conditions Microphone array sound capture can reduce some mismatch by removing ambi ent noise and reverberation but o ers insu cient im provement in performance However using array sig nal capture in conjunction with Hidd...

In this paper, we describe an adaptation method for speech recognition systems that is based on a piecewise-linear approximation to a non-linear transformation of the feature space. The method extends a previously proposed non-linear transformation (NLT) technique by making the transformation function more sophisticated (piecewise-linear instead of piecewiseconstant), and by computing the trans...

This thesis deals with linear transformations at various stages of the automatic speech recognition process. In current state-of-the-art speech recognition systems linear transformations are widely used to care for a potential mismatch of the training and testing data and thus enhance the recognition performance. A large number of approaches has been proposed in literature, though the connectio...

Speaker recognition using support vector machines (SVMs) with features derived from generative models has been shown to perform well. Typically, a universal background model (UBM) is adapted to each utterance yielding a set of features that are used in an SVM. We consider the case where the UBM is a Gaussian mixture model (GMM), and maximum likelihood linear regression (MLLR) adaptation is used...