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Low-Resource Speech Recognition and Keyword-Spotting Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Improved Speaker Adaptation by Combining I-vector and fMLLR with Deep Bottleneck Networks

Authors : Thai Son Nguyen, Kevin Kilgour, Matthias Sperber, Alex Waibel

Published in: Speech and Computer

Publisher: Springer International Publishing

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Abstract

This paper investigates how deep bottleneck neural networks can be used to combine the benefits of both i-vectors and speaker-adaptive feature transformations. We show how a GMM-based speech recognizer can be greatly improved by applying feature-space maximum likelihood linear regression (fMLLR) transformation to outputs of a deep bottleneck neural network trained on a concatenation of regular Mel filterbank features and speaker i-vectors. The addition of the i-vectors reduces word error rate of the GMM system by 3–7% compared to an identical system without i-vectors. We also examine Deep Neural Network (DNN) systems trained on various combinations of i-vectors, fMLLR-transformed bottleneck features and other feature space transformations. The best approach results speaker-adapted DNNs which showed 15–19% relative improvement over a strong speaker-independent DNN baseline.

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Metadata
Title
Improved Speaker Adaptation by Combining I-vector and fMLLR with Deep Bottleneck Networks
Authors
Thai Son Nguyen
Kevin Kilgour
Matthias Sperber
Alex Waibel
Copyright Year
2017
Book
Speech and Computer

Print ISBN: 978-3-319-66428-6

Electronic ISBN: 978-3-319-66429-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-66429-3_41

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