Considering Uncertainty by Particle Filter Enhanced Speech Features in Large Vocabulary Continuous Speech Recognition

The goal of noise compensation techniques is the perfect reconstruction of clean features. Unfortunately, the reconstructed features can not be assumed to be perfect. Therefore, to improve performance, the uncertainty of enhanced speech features should be propagated into the hidden Markov model of automatic speech recognition systems. This paper shows how to jointly estimate the noise and the uncertainty (expressed by the variance) by particle filters in the logarithmic Mel power domain and how to propagate the uncertainty through the front-end into the hidden Markov model. In the experimental section, improvements in word accuracy of a large vocabulary continuous speech recognition system are presented