Robust multi-stream speech recognition based on weighting the output probabilities of feature components

In the traditional multi-stream fusion methods of speech recognition, all the feature components in a data stream share the same stream weight, while their distortion levels are usually different when the speech recognizer works in noisy environments. To overcome this limitation of the traditional multi-stream frameworks, the current study proposes a new stream fusion method that weights not only the stream outputs, but also the output probabilities of feature components. How the stream and feature component weights in the new fusion method affect the decision is analyzed and two stream fusion schemes based on the mariginalisation and soft decision models in the missing data techniques are proposed. Experimental results on the hybrid sub-band multi-stream speech recognizer show that the proposed schemes can adjust the stream influences on the decision adaptively and outperform the traditional multi-stream methods in various noisy environments.