Ensemble integration of calibrated speaker localization and statistical speech detection in domestic environments

This paper describes speaker localization and speech detection techniques for domestic environments. In real environments, it is hard to localize speakers because reverberation causes discrepancy from the simple spherical wave assumption. We propose a template-based method that calibrates the localization errors included in conventional methods. In addition, we use statistical speech detection methods to deal with noises. However, in this challenge, there are five rooms and leaked utterances from other rooms must be rejected. This kind of rejection is hard to perform by only using speech detection results. To address this problem, we also propose a method that integrates speech localization and speech detection using a minimum cost criterion or a classifier-based strategy. The proposed method achieved an accuracy of 0.712 for speaker localization and an F value of 0.743 for speech detection on the development set compared with the baseline 0.559 and 0.570, and 0.666 and 0.706 on the test set compared with the baseline 0.517 and 0.602.