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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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Related topics

Related topics

5 relations

Broader (2)

Automatic identification and data captureSpeech recognition
Feature scalingHidden Markov modelKernel eigenvoice

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2020
2020
Subspace Gaussian mixture based language modeling for large vocabulary continuous speech recognition
2016
2016
Feature mapping using far-field microphones for distant speech recognition
2016
2016
Towards Robust Indonesian Speech Recognition with Spontaneous-Speech Adapted Acoustic Models
2016
2016
DNNs for Unsupervised Extraction of Pseudo FMLLR Features Without Explicit Adaptation Data
In this paper, we propose the use of deep neural networks (DNN) as a regression model to estimate feature-space maximum… 
Highly Cited
2016
Highly Cited
2016
Sequence summarizing neural network for speaker adaptation
In this paper, we propose a DNN adaptation technique, where the i-vector extractor is replaced by a Sequence Summarizing Neural… 
2013
2013
Rapid speaker adaptation in latent speaker space with non-negative matrix factorization
Highly Cited
2013
Highly Cited
2013
Improvements to Deep Convolutional Neural Networks for LVCSR
Deep Convolutional Neural Networks (CNNs) are more powerful than Deep Neural Networks (DNN), as they are able to better reduce… 
2012
2012
Initialization of adaptation by sufficient statistics using phonetic tree
In this work we deal with the problem of small amount of data when estimating a feature transformation for the speaker adaptation… 
2009
2009
Refinement Approach for Adaptation Based on Combination of MAP and fMLLR
This paper deals with a combination of basic adaptation techniques of Hidden Markov Model used in the speech recognition. The… 
2008
2008
Quick fmllr for speaker adaptation in speech recognition
Feature space maximum likelihood linear regression (fMLLR) is a widely used technique for speaker adaptation in HMM-based speech… 
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