Speech recognition in noisy car environment based on OSALPC representation and robust similarity measuring techniques

The performance of the existing speech recognition systems degrades rapidly in the presence of background noise. The OSALPC (One-sided Autocorrelation Linear Predictive Coding) representation of the speech signal has shown to be attractive for speech recognition because of its simplicity and its high recognition performance with respect to the standard LPC in severe conditions of additive white noise. The aim of this paper is twofold: 1) to show that OSALPC also achieves good performance in a case of real noisy speech (in a car environment), and 2) to explore its combination with several robust similarity measuring techniques, showing that its performance improves by using cepstral liftering, dynamic features and multilabeling.