GAUSSIAN MIXTURE MODELS FOR ADAPTATION OF DEEP NEURAL NETWORK ACOUSTIC MODELS IN AUTOMATIC SPEECH RECOGNITION SYSTEMS

GAUSSIAN MIXTURE MODELS FOR ADAPTATION OF DEEP NEURAL NETWORK ACOUSTIC MODELS IN AUTOMATIC SPEECH RECOGNITION SYSTEMS

Subject of Research. We study speaker adaptation of deep neural network (DNN) acoustic models in automatic speech recognition systems. The aim of speaker adaptation techniques is to improve the accuracy of the speech recognition system for a particular speaker. Method. A novel method for training an...

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Bibliographic Details
Main Authors: Natalia A. Tomashenko, Yuri Yu. Khokhlov, Anthony Larcher, Yannick Estève, Yuri N. Matveev
Format: Article
Language:English
Published: Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University) 2016-11-01
Series:Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki
Subjects:
automatic speech recognition (ASR)
acoustic models
speaker adaptation
deep neural networks (DNN)
GMM-derived features
GMMD
maximum a posteriori (MAP)
fMLLR
GMM
acoustic model adaptation
fusion
Online Access:http://ntv.ifmo.ru/file/article/16176.pdf

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