The problem of room localization is to determine where, in a multi-room environment, a person is producing a speech utterance. In our work, we are exploiting the information gained from a network of microphones installed all over a house, where the lack of calibration of the microphone energies creates an additional challenge. This paper compares room localizers based on different features (such as energy and cross-correlation between microphones) and classifiers (such as neural networks and discriminative analysis). In order to evaluate the different room localizers in terms of word accuracy this paper also presents a complete distant speech recognition system which tries to take advantage of synergy between the different components without using any oracle information. Finally, the system is analyzed in terms of computational and time resources.
Bibliographic reference. Morales-Cordovilla, Juan A. / Pessentheiner, Hannes / Hagmüller, Martin / Kubin, Gernot (2014): "Room localization for distant speech recognition", In INTERSPEECH-2014, 2450-2453.