It is well known that the introduction of acoustic background distortion and the variability resulting from environmentally induced stress causes speech recognition algorithms to fail. It is suggested that recent studies based on a Source (reinvalor Frame work can provide the necessary foundation to establish robust speech recognition techniques. This research encompasses three inter-related issues; (i) analysis and modeling of speech characteristics brought on by workload task stress, speaker emotion/stress, or speaking in noise (Lombard effect), (ii) adaptive signal processing methods tailored to speech enhancement and stress equalization, and (iii) formulation of new recognition algorithms which are robust in adverse environments. An overview of a statistical analysis of the SUSAS database is presented. Finally, three novel approaches for signal enhancement and stress equalization have been combined to address the issue of recognition under noisy stressful conditions.