Speech recognition (SR) systems are systems that convert speech signals into text and are widely used in mobile devices, wearable devices, and intelligent living room devices. When SR software recognizes speech in the laboratory or another quiet environment, the recognition accuracy is high. However, when SR software is applied in a complex real-world environment, there may be background noise from birds, machines, and so on, the influence of other speakers, or other adverse factors, which will reduce the recognition accuracy of the SR software. In this work, we propose SR-MT, which is a metamorphic testing (MT) approach to test the robustness of SR software. In SR-MT, we adopt four criteria to analyze the MT results and rank the robustness of the target SR software for speech affected by noise interference and speech variations from level 1 to level 5. SR-MT was evaluated on three real industrial applications: iFLYTEK speech-to-text1, Baidu speech-to-text2, and Google speech-to-text3. We found that on average, 13.5% of the words in the speech could not be recognized correctly when the signal-to-noise ratio reached 10 dB. Similarly, changes in speech speed and tone will also reduce the recognition accuracy of SR software.