This paper addresses the problem of noise robustness of automatic speech recognition (ASR) systems in noisy car environments using a Minimum Mean-Square Error Short-Time Spectral Amplitude Estimator (MMSE-STSA). This was accomplished by the integration of an adaptive time varying Noise Shaping Filter (NSF) with the MMSE-STSA algorithm in order to improve the speech enhancement performance by "whitening" the noisy speech signals. Experiments were conducted using a noisy version of speech signals extracted from the TIMIT database. The proposed NSF-based STSA algorithm is used as a processor of an ASR system in order to evaluate its robustness in severe interfering car noise environments. The HTK Hidden Markov Model Toolkit was used throughout our experiments. Results show that the proposed approach, when included in the frontend of an HTK-based ASR system, outperforms that of the conventional recognition process in severe interfering car noise environments for a wide range of SNRs down to -12 dB using a noisy version of the TIMIT database.