The aim of this study is to address the question of whether the Lombard effect, either natural (an involuntary tendency to raise the uttered speech level in the presence of background noise) or synthetically created, is a remedy for speech communication in noise. To this end, a series of experiments to examine the interference of different noises in synthesizing the Lombard effect is performed. Several steps are proposed; first, a recording session is carried out to prepare a dataset of speech with and without the Lombard effect in a controlled environment. Then, we detect frequency changes at each time point on the 2D speech representation. Determining frequency tracks in a speech signal is performed using McAulay and Quartieri algorithm. To quantify the effect of noise on speech, containing the Lombard effect, an average formant track error is calculated as an objective image quality metric. Three image assessment measures, i.e., SSIM (Structural SIMilarity) index, RMSE (Root Mean Square Error), and dHash (Difference Hash), are employed for that purpose. Moreover, several spectral descriptors are analyzed in the context of Lombard speech and various types of noise to discuss their influence on speech. The investigations are concluded with an initial attempt at automatic noise profiling based on the method developed, followed by pitch modifications of neutral speech signal depending on the profiling result and frequency change trends obtained. An overlap-add synthesis in the STRAIGHT vocoder is used for synthesized speech.