This paper describes a new rapid speaker adaptation algorithm using a small amount of adaptation data. This algorithm, termed adaptation by correlation (ABC), exploits the intrinsic correlation among speech units to update the speech models. The algorithmupdates the means of each Gaussian based on its correlation with means of the Gaussians which are observed in the adaptation data; the updatin...

In this paper, we present new theory and results that combine constrained Maximum Likelihood Linear Regression (MLLR), known as feature space MLLR (fMLLR), a state-of-the-art model adaptation technique, with Dynamic Noise Adaptation (DNA), a state-of-the-art noise adaptation algorithm. We explain how DNA implements a highly non-linear transform on speech model features, and why DNA is better su...

In this paper, a concatenated "super" string model based minimum classification error (MCE) model adaptation approach is described. We show that the error rate minimization in the proposed approach can be formulated into maximizing a special ratio of two positive functions. The proposed string model is used to derive the growth transform based error rate minimization for MCE linear regression (...

Automatic speech recognition (ASR) model adaptation is important to many real-life ASR applications due to the variability of speech. The differences of speaker, bandwidth, context, channel and et al. between speech databases of initial ASR models and application data can be major obstacles to the effectiveness of ASR models. ASR models, therefore, need to be adapted to the application environm...

The DOT1L lysine methyltransferase has emerged as a validated therapeutic target in MLL-rearranged (MLLr) acute leukemias. Although S-adenosylmethionine competitive inhibitors have demonstrated pharmacological proof-of-principle in MLLr-leukemia, these compounds require further optimization to improve cellular potency and pharmacokinetic stability. Limiting DOT1L inhibitor discovery and ligand ...

In this paper, we have integrated in a GMM based speaker identi cation system two di erent techniques: a) Maximum Likelihood Linear Regression (MLLR) transformation which adapts the system to the new environment based on modifying the continuous densities of the GMM mixtures. We apply the MLLR to perform environmental compensation by reducing a mismatch due to channel or additive noise e ects, ...

This paper presents a technique to adapt HMMs to new speakers by using Genetic Algorithms (GAs) in unsupervised mode. The implementation requirements of GAs, such as genetic operators and objective function, have been chosen in order to give more reliability to a global linear transformation matrix. By implementing a ‘survival of the fittest’ strategy, the proposed GA-MLLR approach allows to ma...

This paper describes our efforts in extending a large vocabulary speech recognition system to handle broadcast news transcription. Results using the 1995 DARPA H4 evaluation data set are presented for different front-end analyses and for the use of unsupervised model adaptation using maximum likelihood linear regression (MLLR). The HTK system for the 1996 H4 evaluation is then described. It inc...

This paper presents a geometric constrained transformation approach for fast acoustic adaptation, which improves the modeling resolution of the conventional Maximum Likelihood Linear Regression (MLLR). For this approach, the underlying geometry difference between the seed and the target spaces is exposed and quantified, and used as a prior knowledge to reconstruct refiner transforms. Ignoring d...

In this paper, the theoretical framework of maximum a posteriori linear regression (MAPLR) based variance adaptation for continuous density HMMs is described. In our approach, a class of informative prior distribution for MAPLR based variance adaptation is identified, from which the close form solution of MAPLR based variance adaptation is obtained under its EM formulation. Effects of the propo...