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FMLLR

Feature space Maximum Likelihood Linear Regression (fMLLR) is a widely used technique for speaker adaptation in HMM-based speech recognition.
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Papers overview

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2018
2018
When an automatic speech recognition (ASR) system is deployed for real-world applications, it often receives only one utterance… Expand
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2016
2016
In this paper, we propose the use of deep neural networks (DNN) as a regression model to estimate feature-space maximum… Expand
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2016
2016
Long Short-Term Memory (LSTM) is a particular type of recurrent neural network (RNN) that can model long term temporal dynamics… Expand
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2015
2015
The conventional short-term interval features used by the Deep Neural Networks (DNNs) lack the ability to learn longer term… Expand
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Highly Cited
2013
Highly Cited
2013
Deep Convolutional Neural Networks (CNNs) are more powerful than Deep Neural Networks (DNN), as they are able to better reduce… Expand
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Highly Cited
2013
Highly Cited
2013
We propose to adapt deep neural network (DNN) acoustic models to a target speaker by supplying speaker identity vectors (i… Expand
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2012
2012
In this paper, we propose a novel representation of the FMLLR transform. This is different from the standard FMLLR in that the… Expand
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2011
2011
One of the most utilized adaptation techniques is the feature Maximum Likelihood Linear Regression (fMLLR). In comparison with… Expand
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2009
2009
This paper deals with a combination of basic adaptation techniques of Hidden Markov Model used in the speech recognition. The… Expand
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2008
2008
Feature space maximum likelihood linear regression (fMLLR) is a widely used technique for speaker adaptation in HMM-based speech… Expand
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