Blind estimation of a two-slope feature-domain reverberation model is proposed. The reverberation model is suitable for robust distant-talking automatic speech recognition approaches which use a convolution in the feature domain to characterize the reverberant feature vector sequence, e.g. [1, 2, 3]. Since the model describes the reverberation by a matrix-valued IID Gaussian random process, its statistical properties are completely captured by its mean and variance matrices. The suggested solution for the estimation of the model includes two novel features based on the study of simulated rooms: 1) a solution for blindly determining a twoslope decay model from a single-slope estimate; 2) a variance mask to improve the estimation of the variance matrix. Using the proposed solution, the reverberation model can be estimated during recognition without the need of pre-training or using calibration utterances with known transcription. Connected digit recognition experiments using [3] show that the reverberation models estimated by the proposed approach significantly outperform HMM-based recognizers trained on reverberant data in most environments. 1.

To capture the dispersive effect of reverberation by Hidden Markov Model (HMM)-based distant-talking speech recognition systems, adapting the means of the current HMM state based on the means of the preceding states has been suggested in [1]. In this contribution, we propose to incorporate the reverberation models of [2] into the adaptation approach to describe the effect of reverberation with higher accuracy. Connected-digit recognition experiments in three different rooms confirm that the suggested more accurate reverberation representation leads to a significant performance increase in all investigated environments. 1.