Modeling of speech localization in a multi-talker mixture using periodicity and energy-based auditory features.

A recent study showed that human listeners are able to localize a short speech target simultaneously masked by four speech tokens in reverberation [Kopčo, Best, and Carlile (2010). J. Acoust. Soc. Am. 127, 1450-1457]. Here, an auditory model for solving this task is introduced. The model has three processing stages: (1) extraction of the instantaneous interaural time difference (ITD) information, (2) selection of target-related ITD information ("glimpses") using a template-matching procedure based on periodicity, spectral energy, or both, and (3) target location estimation. The model performance was compared to the human data, and to the performance of a modified model using an ideal binary mask (IBM) at stage (2). The IBM-based model performed similarly to the subjects, indicating that the binaural model is able to accurately estimate source locations. Template matching using spectral energy and using a combination of spectral energy and periodicity achieved good results, while using periodicity alone led to poor results. Particularly, the glimpses extracted from the initial portion of the signal were critical for good performance. Simulation data show that the auditory features investigated here are sufficient to explain human performance in this challenging listening condition and thus may be used in models of auditory scene analysis.