Overcoming the Vector Taylor Series Approximation in Speech Feature Enhancement - A Particle Filter Approach

We present a simple, fast and previously unreported noise compensation method for particle filter (PF) based speech feature enhancement, which outperforms the vector Taylor series noise compensation method used by current PF approaches in terms of speed as well as word error rate. Furthermore, we devise a fast acceptance test that overcomes the particle decimation problem associated with PFs for speech feature enhancement, which makes the particle filter approach computationally more efficient.