Issues with uncertainty decoding for noise robust automatic speech recognition

Interest is growing in a class of robustness algorithms that exploit the notion of uncertainty introduced by environmental noise. The majority of these techniques share the property that the uncertainty of an observation due to noise is propagated to the recogniser, resulting in increased model variances. Using appropriate approximations, efficient implementations may be obtained, with the goal of achieving near model-based performance without the associated computational cost. Unfortunately, uncertainty decoding forms that compute the uncertainty in the front-end and pass this to the decoder may suffer from a theoretical problem in low signal-to-noise ratio conditions. This report discusses how this fundamental issue arises, and demonstrates it through two schemes: SPLICE with uncertainty and front-end Joint uncertainty decoding. A method to mitigate this in the Joint form is presented, as well as how SPLICE implicitly addresses it. However, it is shown that a model-based Joint uncertainty decoding approach does not suffer from this limitation, like these front-end forms do, and is also competitive computationally. The issues described and performance of the various schemes are examined on two artificially corrupted corpora: AURORA 2.0 digit recognition database and the thousand-word Resource Management task.