Exhibition Program

We propose a method to accurately estimate “who spoke when" based on speaker's voice characteristics. It works even in a situation where multiple speaker’s speech signals overlap, and accurately counts the number of speakers in such cases. Conventional methods with the similar functionality works only when the observed signal satisfies certain a priori (unrealistic) assumptions (e.g. the number of speaker known in advance, speakers never change their locations). However, these assumptions cannot be often satisfied in realistic scenarios, which leads to performance degradation. On the other hand, the proposed method, which is based purely on deep learning, can theoretically learn and deal with any realistic conversation situations. It is expected to serve as a fundamental technology for automatic conversation analysis systems, and will contribute to realization of automatic meeting minutes generation systems and communication robots.