Relative transfer function modeling for supervised source localization

Speaker localization is one of the most prevalent problems in speech processing. Despite significant efforts in the last decades, high reverberation level still limits the performance of localization algorithms. Furthermore, using conventional localization methods, the information that can be extracted from dual microphone measurements is restricted to the time difference of arrival (TDOA). Under far-field regime, this is equivalent to either azimuth or elevation angles estimation. Full description of speaker's coordinates necessitates several microphones. In this contribution we tackle these two limitations by taking a manifold learning perspective for system identification. We present a training-based algorithm, motivated by the concept of diffusion maps, that aims at recovering the fundamental controlling parameters driving the measurements. This approach turns out to be more robust to reverberation, and capable of recovering the speech source location using merely two microphones signals.