NDLI: Speaker adaptive joint training of Gaussian mixture models and bottleneck features

Content Provider	IEEE Xplore Digital Library
Author	Tuske, Zoltan Golik, Pavel Schluter, Ralf Ney, Hermann
Copyright Year	2015
Description	Author affiliation: Human Language Technology and Pattern Recognition, Computer Science Department, RWTH Aachen University, 52056 Aachen, Germany (Tuske, Zoltan; Golik, Pavel; Schluter, Ralf; Ney, Hermann)
Abstract	In the tandem approach, the output of a neural network (NN) serves as input features to a Gaussian mixture model (GMM) aiming to improve the emission probability estimates. As has been shown in our previous work, GMM with pooled covariance matrix can be integrated into a neural network framework as a softmax layer with hidden variables, which allows for joint estimation of both neural network and Gaussian mixture parameters. Here, this approach is extended to include speaker adaptive training (SAT) by introducing a speaker dependent neural network layer. Error backpropagation beyond this speaker dependent layer realizes the adaptive training of the Gaussian parameters as well as the optimization of the bottleneck (BN) tandem features of the underlying acoustic model, simultaneously. In this study, after the initialization by constrained maximum likelihood linear regression (CMLLR) the speaker dependent layer itself is kept constant during the joint training. Experiments show that the deeper backpropagation through the speaker dependent layer is necessary for improved recognition performance. The speaker adaptively and jointly trained BN-GMM results in 5% relative improvement over very strong speaker-independent hybrid baseline on the Quaero English broadcast news and conversations task, and on the 300-hour Switchboard task.
Starting Page	596
Ending Page	603
File Size	249551
Page Count	8
File Format	PDF
e-ISBN	9781479972913
DOI	10.1109/ASRU.2015.7404850
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models Training Acoustics Hidden Markov models Robustness Artificial neural networks SAT MLP GMM log-Linear mixture model joint-training unsupervised adaptation CMLLR
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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