Robust speech recognition using probabilistic union models

This paper introduces a new statistical approach, namely the probabilistic union model, for speech recognition involving partial, unknown frequency-band corruption. Partial frequency-band corruption accounts for the effect of a family of real-world noises. Previous methods based on the missing feature theory usually require the identity of the noisy bands. This identification can be difficult for unexpected noise with unknown, time-varying band characteristics. The new model combines the local frequency-band information based on the union of random events, to reduce the dependence of the model on information about the noise. This model partially accomplishes the target: offering robustness to partial frequency-band corruption, while requiring no information about the noise. This paper introduces the theory and implementation of the union model, and is focused on several important advances. These new developments include a new algorithm for automatic order selection, a generalization of the modeling principle to accommodate partial feature stream corruption, and a combination of the union model with conventional noise reduction techniques to deal with a mixture of stationary noise and unknown, nonstationary noise. For the evaluation, we used the TIDIGITS database for speaker-independent connected digit recognition. The utterances were corrupted by various types of additive noise, stationary or time-varying, assuming no knowledge about the noise characteristics. The results indicate that the new model offers significantly improved robustness in comparison to other models.