On the robustness of linear discriminant analysis as a preprocessing step for noisy speech recognition

This paper addresses the problem of speech recognition in a noisy environment by finding a robust speech parametric space. The framework of linear discriminant analysis (LDA) is used to derive an efficient speech parametric space for noisy speech recognition, from a classical static+dynamic MFCC space. We first show that the derived LDA space can lead to a higher discrimination than the MFCC related space, even at low signal-to-noise ratio (SNR). Then, we test the robustness of the LDA space to variations between the training and testing SNR. Experiments are performed on a continuous speech recognition task, where speech is degraded with various noise sources: Gaussian noise, F16, Lynx helicopter, autobus, hair dryer. It was found that LDA is highly sensitive to SNR variations for white noise (Gaussian, hair dryer), while remaining quite efficient for the others