A comparative study of techniques for HMM-based noisy speech recognition in noisy car environment

Abstract

The performance of existing speech recognition systems degrades rapidly in the presence of background noise when training and testing cannot be done under the same ambient conditions. The aim of this paper is to report the application of several robust techniques on a system based on the HMM (Hidden Markov Models) and VQ (Vector Quantization) approaches for speech recognition in noisy car environment: parameterization based on the linear prediction of the causal part of the autocorrelation sequence (OSALPC) - proposed by the authors in [1] [2]-, optimization of spectral model order and cepstral lifter, cepstral projection distance measure, dynamic information and multilabeling.