On the Statistical Properties of Reverberant Speech Feature Vector Sequences

Conference contribution


Publication Details

Author(s): Sehr A, Kellermann W
Publishing place: Tel Aviv, Israel
Publication year: 2010
Conference Proceedings Title: Proc. International Workshop on Acoustic Echo and Noise Control (IWAENC)
Language: English


Abstract

The statistical properties of reverberant logarithmic mel-spectral feature vector sequences that are relevant for acoustic mod-eling in robust speech recognition are analyzed and compared to the corresponding properties of clean-speech feature vector sequences. The investigation focuses on probability densities of feature vector elements and the correlation between feature vectors of different frames. A Monte-Carlo method is used for a quantitative analysis of the density changes and the increase in inter-frame correlation due to reverberation. As an example for the insights that can be obtained from this analysis, the densities and inter-frame correlations of reverberant features are compared to the modeling capabilities of reverberantly-trained and adapted HMMs. Thus, limitations of these approaches can be clearly identified.


FAU Authors / FAU Editors

Kellermann, Walter Prof. Dr.-Ing.
Professur für Nachrichtentechnik


How to cite

APA:
Sehr, A., & Kellermann, W. (2010). On the Statistical Properties of Reverberant Speech Feature Vector Sequences. In Proc. International Workshop on Acoustic Echo and Noise Control (IWAENC). Tel Aviv, IL: Tel Aviv, Israel.

MLA:
Sehr, Armin, and Walter Kellermann. "On the Statistical Properties of Reverberant Speech Feature Vector Sequences." Proceedings of the Proc. International Workshop on Acoustic Echo and Noise Control (IWAENC), Tel Aviv Tel Aviv, Israel, 2010.

BibTeX: 

Last updated on 2019-08-05 at 15:45