In this paper we present our recent study on the impact of speaking mode variabilities on speech recognition by surface electromyography (EMG). Surface electromyography captures the electric potentials of the human articulatory muscles, which enables a user to communicate naturally without making any audible sound. Our previous experiments have shown that the EMG signal varies greatly between different speaking modes, like audibly uttered speech and silently articulated speech. In this study we extend our previous research and quantify the impact of different speaking modes by investigating the amount of mode-specific leaves in phonetic decision trees. We show that this measure correlates highly with discrepancies in the spectral energy of the EMG signal, as well as with differences in the performance of a recognizer on different speaking modes. We furthermore present how EMG signal adaptation by spectral mapping decreases the effect of the speaking mode.