A speech event detection and localization task for multiroom environments

Domestic environments are particularly challenging for distant speech recognition and audio processing in general. Reverberation, background noise and interfering sources, as well as the propagation of acoustic events across adjacent rooms, critically degrade the performance of standard speech processing algorithms. The DIRHA EU project addresses the development of distant-speech interaction with devices and services within the multiple rooms of typical apartments. A corpus of multichannel acoustic data has been created to represent realistic acoustic scenes, of different degrees of complexity, occurring in such an environment. It includes multichannel simulations based on measured impulse responses and real data collected in the same apartment. A basic but fundamental task of the front-end processing enabling effective ASR is the detection and localization of speech events generated by users, without constraints on their position or orientation within the various rooms. In this paper we describe the acoustic corpus and present a baseline approach to the joint task of speech detection and source localization, using speech related features such as pitch, combined with features derived from spatial coherence.