Band importance for speech-in-speech recognition

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 2kHz may contribute more to speech recognition in two-talker speech than in speech-shaped noise.