Combining missing-data reconstruction and uncertainty decoding for robust speech recognition

This paper proposes a novel approach for noise-robust speech recognition which combines a missing-data (MD) derived spectral reconstruction technique and uncertainty decoding based on the weighted Viterbi algorithm (WVA). First, the noisy feature vectors are compensated by using a novel MD imputation technique based on the integration of truncated Gaussian pdfs. Although the proposed MD estimator has both the advantages of MD techniques and the use of cepstral features, it may still be affected by a number of uncertainty sources. In order to deal with these uncertainties, WVA-based uncertainty decoding is proposed. Our experiments on the Aurora-2 and Aurora-4 tasks show that the proposed MD estimator outperforms other MD imputation techniques. Also, we show that the combination of MD imputation with WVA provides better results than the combination with other uncertainty processing techniques such as the use of evidence pdfs for the estimated features.