A Combined Approach for Estimating a Feature-domain Reverberation Model in Non-diffuse Environments

A combined approach for estimating a feature-domain reverberation model suitable for the robust distant-talking automatic speech recognition concept REMOS (REverberation MOdeling for Speech recognition) [1] is proposed. Based on a few calibration utterances recorded in the target environment, the combined approach employs ML estimation and blind estimation of the reverberation time to determine a two-slope reverberation model. Since measurements of room impulse responses become unnecessary, the effort for training is greatly reduced compared to [1] and compared to training HMMs on artificially reverberated data. Connected digit recognition experiments show that the proposed reverberation models in connection with the REMOS concept significantly outperform HMM-based recognizers trained on reverberant data.