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Performance of current speech recognition systems is significantly deteriorated when exposed to strongly noisy environment. It can be attributed to background noise and Lombard effect (LE). Attempts for LE-robust systems often display a tradeoff between LE-specific improvements and the portability to neutral speech. Therefore, towards LE-robust recognition, it seems effective to use a set of conditions-dedicated subsystems driven by a condition classifier, rather than attempting for one universal recognizer.