Modeling speech localization, talker identification, and word recognition in a multi-talker setting.

This study introduces a model for solving three different auditory tasks in a multi-talker setting: target localization, target identification, and word recognition. The model was used to simulate psychoacoustic data from a call-sign-based listening test involving multiple spatially separated talkers [Brungart and Simpson (2007). Percept. Psychophys. 69(1), 79-91]. The main characteristics of the model are (i) the extraction of salient auditory features ("glimpses") from the multi-talker signal and (ii) the use of a classification method that finds the best target hypothesis by comparing feature templates from clean target signals to the glimpses derived from the multi-talker mixture. The four features used were periodicity, periodic energy, and periodicity-based interaural time and level differences. The model results widely exceeded probability of chance for all subtasks and conditions, and generally coincided strongly with the subject data. This indicates that, despite their sparsity, glimpses provide sufficient information about a complex auditory scene. This also suggests that complex source superposition models may not be needed for auditory scene analysis. Instead, simple models of clean speech may be sufficient to decode even complex multi-talker scenes.