Publication Details
Training Data Augmentation and Data Selection
Veselý Karel, Ing., Ph.D. (DCGM)
Žmolíková Kateřina, Ing., Ph.D. (FIT)
Delcroix Marc (FIT)
Watanabe Shinji (FIT)
Burget Lukáš, doc. Ing., Ph.D. (DCGM)
Černocký Jan, prof. Dr. Ing. (DCGM)
Szőke Igor, Ing., Ph.D. (DCGM)
training data, augmentation, data selection
Data augmentation is a simple and efficient technique to improve the robustness of a speech recognizer when deployed in mismatched training-test conditions. Our work, conducted during the JSALT 2015 workshop, aimed at the development of: (1) Data augmentation strategies including noising and reverberation. They were tested in combination with two approaches to signal enhancement: a carefully engineered WPE dereverberation and a learned DNN-based denoising autoencoder. (2) Proposing a novel technique for extracting an informative vector from a Sequence Summarizing Neural Network (SSNN). Similarly to i-vector extractor, the SSNN produces a "summary vector", representing an acoustic summary of an utterance. Such vector can be used directly for adaptation, but the main usage matching the aim of this chapter is for selection of augmented training data. All techniques were tested on the AMI training set and CHiME3 test set.
This book covers the state-of-the-art in deep neural-network-based methods for noise robustness in distant speech recognition applications. It provides insights and detailed descriptions of some of the new concepts and key technologies in the field, including novel architectures for speech enhancement, microphone arrays, robust features, acoustic model adaptation, training data augmentation, and training criteria. The contributed chapters also include descriptions of real-world applications, benchmark tools and datasets widely used in the field. Chapter 10 is about the Training Data Augmentation and Data Selection.
This book is intended for researchers and practitioners working in the field of speech processing and recognition who are interested in the latest deep learning techniques for noise robustness. It will also be of interest to graduate students in electrical engineering or computer science, who will find it a useful guide to this field of research.
@inbook{BUT144497,
author="Martin {Karafiát} and Karel {Veselý} and Kateřina {Žmolíková} and Marc {Delcroix} and Shinji {Watanabe} and Lukáš {Burget} and Jan {Černocký} and Igor {Szőke}",
title="Training Data Augmentation and Data Selection",
booktitle="New Era for Robust Speech Recognition: Exploiting Deep Learning",
year="2017",
publisher="Springer International Publishing",
address="Heidelberg",
series="Computer Science, Artificial Intelligence",
pages="245--260",
doi="10.1007/978-3-319-64680-0\{_}10",
isbn="978-3-319-64679-4",
url="http://www.springer.com/gp/book/9783319646794#aboutBook"
}