Speech recognition in impulsive noise

This paper presents experimental results on the use of noise compensation schemes with hidden Markov model (HMM) speech recognition systems operating in the presence of impulsive noise. A measure of signal to impulsive noise ratio is introduced, and the effects of varying the percentage of impulsive noise contamination, and the power of impulsive noise, on speech recognition are investigated. For the modelling of an impulsive noise process, an amplitude-modulated binary sequence model and a binary-state HMM are considered. For impulsive noise compensation a front-end method and a noise-adaptive method are evaluated. Experiments demonstrate that the noise compensation methods achieve a substantial improvement in speech recognition accuracy across a wide range of signal to impulsive noise ratios.