Add to ORKGA speaker clustering algorithm is presented that is based on an eigenspace representation of Maximum Likelihood Linear Regression (MLLR) transformations and is used for training cluster-dependent regression-class trees for MLLR adaptation. It is shown that significant automatic speech recognition (ASR) system performance gains are possible by choosing the best regression-class tree structure for individual speakers. To take advantage of the potential gains, an algorithm for combining the MLLR mean transformations from cluster-specific trees is described that effectively results in a soft regression-class tree. In conversational speech recognition, only small overall improvements are obtained, but the number of speakers that have performance...
We extend the well-known technique of constrained Maximum Likelihood Linear Regression (MLLR) to com...
To recognize non-native speech, larger acoustic/linguistic distor-tions must be handled adequately i...
This paper considers the problem of speaker adaptation of acous-tic models in speech recognition. We...
Abstract. In this paper, we propose a model-based hierarchical clustering algorithm that automatical...
Speaker clustering is one of the major methods for speaker adaptation. MLLR (Maximum Likelihood Line...
Abstract — Speaker space based adaptation methods for auto-matic speech recognition have been shown ...
In this paper, we propose an application of kernel methods for fast speaker adaptation based on, ker...
This paper presents a technical speaker adaptation method called WMLLR, which is based on maximum li...
In this paper an effective technique for speaker adaptation on the feature domain is presented. This...
The challenge of speaker adaptation is to reliably fine-tune models of a general population to fit t...
In this paper, we propose a novel speaker adaptation technique, regularized-MLLR, for Computer Assis...
The goal of this thesis is to find new and efficient features for speaker recognition. We are mostly...
The work presented in this report focuses on an essential problem when doing speaker adaptation; nam...
For the problem of speaker adaptation in speech recognition, the performance depends on the availabi...
A robust ASR system needs to perform well in different environment and with different speakers. For ...
We extend the well-known technique of constrained Maximum Likelihood Linear Regression (MLLR) to com...
To recognize non-native speech, larger acoustic/linguistic distor-tions must be handled adequately i...
This paper considers the problem of speaker adaptation of acous-tic models in speech recognition. We...
Abstract. In this paper, we propose a model-based hierarchical clustering algorithm that automatical...
Speaker clustering is one of the major methods for speaker adaptation. MLLR (Maximum Likelihood Line...
Abstract — Speaker space based adaptation methods for auto-matic speech recognition have been shown ...
In this paper, we propose an application of kernel methods for fast speaker adaptation based on, ker...
This paper presents a technical speaker adaptation method called WMLLR, which is based on maximum li...
In this paper an effective technique for speaker adaptation on the feature domain is presented. This...
The challenge of speaker adaptation is to reliably fine-tune models of a general population to fit t...
In this paper, we propose a novel speaker adaptation technique, regularized-MLLR, for Computer Assis...
The goal of this thesis is to find new and efficient features for speaker recognition. We are mostly...
The work presented in this report focuses on an essential problem when doing speaker adaptation; nam...
For the problem of speaker adaptation in speech recognition, the performance depends on the availabi...
A robust ASR system needs to perform well in different environment and with different speakers. For ...
We extend the well-known technique of constrained Maximum Likelihood Linear Regression (MLLR) to com...
To recognize non-native speech, larger acoustic/linguistic distor-tions must be handled adequately i...
This paper considers the problem of speaker adaptation of acous-tic models in speech recognition. We...