This paper overviews the main technologies that have recently been developed for making speech recognition systems more robust at both the acoustic and linguistic processing levels. These technologies are reviewed from the viewpoint of a stochastic pattern matching paradigm for speech recognition. Improved robustness enables better speech recognition over a wide range of unexpected and adverse conditions by reducing mismatches between training and testing speech utterances. This paper focuses on supervised vs. unsupervised adaptation techniques, the Bayesian adaptive learning approach, the minimum classification error (MCE/GPD) approach, the parallel model combination (PMC, HMM composition) technique, and spontaneous speech recognition techniques.