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This paper investigates the modeling and estimation of spectral parameters at formants of noisy speech in the presence of car and train noise. Formant estimation using two-dimensional hidden Markov models (2D-HMM) is reviewed and employed to study the influence of noise on observations of formants. The first set of experimental results presented show the influence of car and train noise on the distribution and the estimates of the formant trajectories. Due to the shapes of the spectra of speech and car/train noise, the 1st formant is most affected by noise and the last formant is least affected. The effects of inclusion of formant features in speech recognition at different SNRs are presented. It is shown that formant features provide better performance at low SNRs compared to MFCC features. Finally, for robust estimation of noisy speech, a formant tracking method based on combination of LP-spectral subtraction and Kalman filter is presented. Average formant tracking errors at different SNRs are computed and the results show that after noise reduction the formant tracking errors of 1st formant are reduced by 60%. The de-noised formant tracking LP models can be used for recognition and/or enhancement of noisy speech.