Semi-Blind Model Adaptation using Piece-wise Energy Decay Curve for Large Reverberant Environments

This work presents semi-blind acoustic model adaptation based on a piece-wise energy decay curve. The dual slope representation of the piece-wise curve accurately captures the early and late reflection decay that helps in precisely modeling the smearing effect caused due to reverberation. The slopes are estimated in a semi-blind fashion, late reflection slope is estimated blindly by finding the highest likelihood obtained after matching the test features with Gaussian mixture models trained on reverberant data, while the early reflection slope is empirically computed. Adaptation using piece-wise decay curve leads to robust acoustic models consequently improving the recognition performance. The approach is tested on connected digits recognition task in a large room with various reverberation times. The performance is compared with the exponential decay approach and incremental MLLR, where the proposed approach is found to provide robust and consistent gains across all the cases.