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Robot audition systems require capabilities for sound source separation and the recognition of separated sounds, since we hear a mixture of sounds in our daily lives, especially mixed of speech. We report a robot audition system with a pair of omni-directional microphones embedded in a humanoid that recognizes two simultaneous talkers. It first separates the sound sources by Independent Component Analysis (ICA) with the single-input multiple-output (SIMO) model. Then, spectral distortion in the separated sounds is then estimated to generate missing feature masks. Finally, the separated sounds are recognized by missing-feature theory (MFT) for Automatic Speech Recognition (ASR). The novel aspects of our system involve estimates of spectral distortion in the temporal-frequency domain in terms of feature vectors and based on estimates error in SIMO-ICA signals. The resulting system outperformed the baseline robot audition system by 7%.