We propose a model of spectral contextual effects to simulate the superior recognition ability of humans and apply it to a front-end processor for word spotting. This model assumes that perceived spectra are influenced by adjacent spectral peaks and that the magnitude of the influence can be estimated by the minimum classification error criterion. Three experiments were carried out to evaluate the performance of the model. The results show that the model can compensate for neutralized spectra and bring them to their typical patterns. This improves word spotting accuracy.