We propose a novel particle filter approach to enhancing speech features for robust speech recognition. We use particle filters to compensate the corrupted features according to an additive noise distortion model by incorporating both the statistics from the clean speech Hidden Markov Models and of the observed background noise to map the noisy features back to clean speech features. We report on experimental results obtained with the Aurora-2 connected digit recognition task, and show that a large digit error reduction of 67% from multi-condition training is attainable if the missing side information needed for particle filter based compensation were available. When such nuisance parameters are estimated in actual operational conditions then an error reduction of only 13% is currently achievable. We anticipate more improvements in the future when better estimation algorithms are explored.
Bibliographic reference. Mushtaq, Aleem / Tsao, Yu / Hui-Lee, Chin (2010): "A particle filter feature compensation approach to robust speech recognition", In INTERSPEECH-2010, 2054-2057.