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This study analyzes the impact of noisy background variations and Lombard effect (LE) on large vocabulary continuous speech recognition (LVCSR). Robustness of several front-end feature extraction strategies combined with state-of-the-art feature distribution normalizations is tested on neutral and Lombard speech from the UT-Scope database presented in two types of background noise at various levels of SNR. An extension of a bottleneck (BN) front-end utilizing normalization of both critical band energies (CRBE) and BN outputs is proposed and shown to provide a competitive performance compared to the best MFCC-based system. A novel MFCC-based BN front-end is introduced and shown to outperform all other systems in all conditions considered (average 4.1% absolute WER reduction over the second best system). Additionally, two phenomena are observed: (i) combination of cepstral mean subtraction and recently established RASTALP filtering significantly reduces transient effects of RASTA band-pass filtering and increases ASR robustness to noise and LE; (ii) histogram equalization may benefit from utilizing reference distributions derived from pre-normalized rather than raw training features, and also from adopting distributions from different front-ends.