Semi-Blind Source Separation using Binary Masking and Independent Vector Analysis

Recent prevalence of speech recognition system increases the opportunity of simultaneous recognition of multiple speakers' utterances. There are two types of source separation methods: physical and statistical. The former is based on the physical information such as a direction of arrival of sound sources. The latter only uses statistical independence. The advantage of the former is fast computation and effectiveness with precise information; and that of the latter is no need for physical information, which leads to the robustness of measurement errors. In this paper, we propose to combine these approaches effectively. Experiments on a speech recognition task show that the proposed method can achieve the upper limit performance of the two approaches. (c) 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.