Eigen-MLLR coe cients are proposed as new feature parameters for speaker-identi cation in this paper. By performing principle component analysis on MLLR parameters among training speakers, the eigen-MLLR coe cients (EMCs) are derived as the coe cients for the eigenvectors. The discriminating function of the new EMC features based on the Fisher criterion is found to be ten times larger than that...

One of the most popular approaches to parameter adaptation in hidden Markov model (HMM) based systems is the maximum likelihood linear regression (MLLR) technique. In our previous work, we proposed factored MLLR (FMLLR) where MLLR parameter is defined as a function of a control parameter vector. We presented a method to train the FMLLR parameters based on a general framework of the expectationm...

This paper investigates the use of maximum likelihood linear regression (MLLR) for both speaker and environment adaptation. MLLR transforms the mean and variance parameters of a set of HMMs. In this paper a number of different types of linear transformations of the variances are examined including full, block diagonal, and diagonal transformation matrices. Experiments on large vocabulary speake...

This paper presents a technical speaker adaptation method called WMLLR, which is based on maximum likelihood linear regression (MLLR). In MLLR, a linear regression-based transform which adapted the HMM mean vectors was calculated to maximize the likelihood of adaptation data. In this paper, the prior knowledge of the initial model is adequately incorporated into the adaptation. A series of spea...

In the past few years, transformation-based model adaptation techniques have been widely used to help reducing acoustic mismatch between training and testing conditions of automatic speech recognizers. The estimation of the transformation parameters is usually carried out using estimation paradigms based on classical statistics such as maximum likelihood, mainly because of their conceptual and ...

A new approach for speaker adaptation consisting of MLLR adaptation enriched by a special weighting scheme followed by MAP adaptation is presented. While the standard MLLR approach increases the error rate for the considered small amounts of adaptation data in on-line, unsupervised adaptation, our approach can reduce the error by up to 30%. This result can further be improved by switching to MA...

To recognize non-native speech, larger acoustic/linguistic distortions must be handled adequately in acoustic modeling, language modeling, lexical modeling, and/or decoding strategy. In this paper, a novel method to enhance MLLR adaptation of acoustic models for non-native speech recognition is proposed. In the case of native speech recognition, MLLR speaker adaptation was successfully introduc...

In the framework of a Bayesian classifier based on mixtures of gaussians, we address the problem of non-frontal face verification (when only a single (frontal) training image is available) by extending each frontal face model with artificially synthesized models for non-frontal views. The synthesis methods are based on several implementations of Maximum Likelihood Linear Regression (MLLR), as w...

Automatic speech recognition (ASR) is currently used in many assistive technologies, such as helping individuals with speech impairment in their communication ability. One challenge in ASR for speech-impaired individuals is the difficulty in obtaining a good speech database of impaired speakers for building an effective speech acoustic model. Because there are very few existing databases of imp...