Robust Speech Recognition of Uncertain or Missing Data

1. Introduction
Reinhold Haeb-Umbach, Dorothea Kolossa

2. Uncertainty Decoding and Conditional Bayesian Estimation
Reinhold Haeb-Umbach

3. Uncertainty Propagation
Ramón Fernandez Astudillo, Dorothea Kolossa

4. Front-End, Back-End, and Hybrid Techniques for Noise-Robust Speech Recognition
Li Deng

5. Model-Based Approaches to Handling Uncertainty
M. J. F. Gales

6. Reconstructing Noise-Corrupted Spectrographic Components for Robust Speech Recognition
Bhiksha Raj, Rita Singh

7. Automatic Speech Recognition Using Missing Data Techniques: Handling of Real-World Data
Jort F. Gemmeke, Maarten Segbroeck, Yujun Wang, Bert Cranen, Hugo hamme

8. Conditional Bayesian Estimation Employing a Phase-Sensitive Observation Model for Noise Robust Speech Recognition
Volker Leutnant, Reinhold Haeb-Umbach

9. Variance Compensation for Recognition of Reverberant Speech with Dereverberation Preprocessing
Marc Delcroix, Shinji Watanabe, Tomohiro Nakatani

10. A Model-Based Approach to Joint Compensation of Noise and Reverberation for Speech Recognition
Alexander Krueger, Reinhold Haeb-Umbach

11. Evidence Modeling for Missing Data Speech Recognition Using Small Microphone Arrays
Marco Kühne, Roberto Togneri, Sven Nordholm

12. Recognition of Multiple Speech Sources Using ICA
Eugen Hoffmann, Dorothea Kolossa, Reinhold Orglmeister

13. Use of Missing and Unreliable Data for Audiovisual Speech Recognition
Alexander Vorwerk, Steffen Zeiler, Dorothea Kolossa, Ramón Fernandez Astudillo, Dennis Lerch

Nyckelord: Engineering, Signal, Image and Speech Processing, Artificial Intelligence (incl. Robotics), Computational Linguistics