Robust Speech Recognition of Uncertain or Missing Data Theory and Applications /
Automatic speech recognition suffers from a lack of robustness with respect to noise, reverberation and interfering speech. The growing field of speech recognition in the presence of missing or uncertain input data seeks to ameliorate those problems by using not only a preprocessed speech signal but...
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| Format: | Electronic eBook |
| Language: | English |
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Berlin, Heidelberg :
Springer Berlin Heidelberg,
2011.
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| Online Access: | Full Text via HEAL-Link |
Table of Contents:
- Chap. 1 – Introduction
- Part I – Theoretical Foundations
- Chap. 2 – Uncertainty Decoding and Conditional Bayesian Estimation
- Chap. 3 – Uncertainty Propagation
- Part II – Applications
- Chap. 4 – Front-End, Back-End, and Hybrid Techniques for Noise-Robust Speech Recognition
- Chap. 5 – Model-Based Approaches to Handling Uncertainty
- Chap. 6 – Reconstructing Noise-Corrupted Spectrographic Components for Robust Speech Recognition
- Chap. 7 – Automatic Speech Recognition Using Missing Data Techniques: Handling of Real-World Data
- Chap. 8 – Conditional Bayesian Estimation Employing a Phase-Sensitive Estimation Model for Noise-Robust Speech Recognition.- Part III – Reverberation Robustness
- Chap. 9 – Variance Compensation for Recognition of Reverberant Speech with Dereverberation Processing
- Chap. 10 – A Model-Based Approach to Joint Compensation of Noise and Reverberation for Speech Recognition
- Part IV – Applications: Multiple Speakers and Modalities
- Chap. 11 – Evidence Modelling for Missing Data Speech Recognition Using Small Microphone Arrays
- Chap. 12 – Recognition of Multiple Speech Sources Using ICA.- Chap. 13 – Use of Missing and Unreliable Data for Audiovisual Speech Recognition.- Index.
