Maximum likelihood estimation of a reverberation model for robust distant-talking speech recognition

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Sehr A, Zheng Y, Nöth E, Kellermann W
Publication year: 2007
Conference Proceedings Title: Proc. European Signal Processing Conference
Pages range: 1299-1303
ISBN: 9788392134022


Abstract


We propose a novel approach for estimating a reverberation model for a robust recognizer according to [1], which is designed to allow distant-talking automatic speech recognition (ASR) in reverberant environments. Based on a few calibration utterances with known transcriptions recorded in the target environment, a maximum likelihood estimator is used to find the means and variances of the reverberation model. In contrast to [1] and to HMM training on artificially reverberated training data (e. g. [2]), measurements of room impulse responses become unnecessary, and the effort for training is greatly reduced. Simulations of a connected digit recognition task show that, in highly reverberant environments, the reverberation models estimated by the proposed approach achieve significantly higher recognition rates than HMMs trained on reverberant data. © 2007 EURASIP.



FAU Authors / FAU Editors

Kellermann, Walter Prof. Dr.-Ing.
Professur für Nachrichtentechnik
Nöth, Elmar Prof. Dr.-Ing.
Professur für Informatik (Mustererkennung)
Sehr, Armin Dr.-Ing.
Professur für Nachrichtentechnik
Zheng, Yuanhang
Professur für Nachrichtentechnik


How to cite

APA:
Sehr, A., Zheng, Y., Nöth, E., & Kellermann, W. (2007). Maximum likelihood estimation of a reverberation model for robust distant-talking speech recognition. In Proc. European Signal Processing Conference (pp. 1299-1303). Poznan, PL.

MLA:
Sehr, Armin, et al. "Maximum likelihood estimation of a reverberation model for robust distant-talking speech recognition." Proceedings of the 15th European Signal Processing Conference, EUSIPCO 2007, Poznan 2007. 1299-1303.

BibTeX: 

Last updated on 2018-08-08 at 22:25