Combining window predictions efficiently - A new imputation approach for noise robust automatic speech recognition
This paper introduces a new optimization-based approach to Sparse Imputation/spectral denoising for robust Automatic Speech Recognition (ASR) applications. In particular, we propose an algorithm which couples frame-level optimization and strategic reconciliation of the predictions in a tight manner. We demonstrate that the proposed algorithm outperforms the current state-of-the-art two-step strategy of first optimizing and then averaging across windows, while maintaining the complexity advantages of efficient techniques like the Elastic Net. Our algorithm is also theoretically able to better exploit the properties of a collinear dictionary, which occurs with spectral exemplars from most speech corpora. Through experiments on the Aurora 2.0 noisy digits database, we demonstrate that this new technique achieves significant performance gains (7.67% on average over various SNR levels) over just simply averaging across large number of predictions.