Prof. Dr.-Ing. Dorothea Kolossa is a professor at the Institut fur Kommunikationsakustik of the Ruhr-Universitat Bochum, Germany; her research interests are automatic speech recognition, digital speech signal processing, and blind source separation. Prof. Dr.-Ing. Reinhold Haeb-Umbach heads the Dept. of Communications Engineering of the University of Paderborn, Germany; his research interest are speech signal processing and automatic speech recognition, statistical learning and pattern recognition, and signal processing for digital communications.

• 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.

Prof. Dr.-Ing. Dorothea Kolossa is a professor at the Institut fur Kommunikationsakustik of the Ruhr-Universitat Bochum, Germany; her research interests are automatic speech recognition, digital speech signal processing, and blind source separation. Prof. Dr.-Ing. Reinhold Haeb-Umbach heads the Dept. of Communications Engineering of the University of Paderborn, Germany; his research interest are speech signal processing and automatic speech recognition, statistical learning and pattern recognition, and signal processing for digital communications.

• 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.

Prof. Dr.-Ing. Dorothea Kolossa is a professor at the Institut fur Kommunikationsakustik of the Ruhr-Universitat Bochum, Germany; her research interests are automatic speech recognition, digital speech signal processing, and blind source separation. Prof. Dr.-Ing. Reinhold Haeb-Umbach heads the Dept. of Communications Engineering of the University of Paderborn, Germany; his research interest are speech signal processing and automatic speech recognition, statistical learning and pattern recognition, and signal processing for digital communications.

• 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.