Predicting masked speech perception typically relies on estimates of the spectral distribution of cues supporting recognition. Current methods for estimating band importance for speech-in-noise use filtered stimuli. These methods are not appropriate for speech-in-speech because filtering can modify stimulus features affecting auditory stream segregation. Here, band importance is estimated by quantifying the relationship between speech recognition accuracy for full-spectrum speech and the target-to-masker ratio by channel at the output of an auditory filterbank. Preliminary results provide support for this approach and indicate that frequencies below 2 kHz may contribute more to speech recognition in two-talker speech than in speech-shaped noise.