Effect of Sound Source Location on Accuracy in Diarization in Multi-Person Dialogue Environment

Abstract

In recent years, the importance of a speech segment detection technique called speaker di-(breakpoint)arization has increased, mainly in conferences, news, and telephone calls. However, conventional speaker segmentation detection methods using neural networks require a huge amount of training data. In this study, training data was created by recording the speech of two speakers of the same gender, splitting and combining them to create a synthetic utterance. The effect of the distance and angle to the microphone on the accuracy of the test data was examined in tests with non-synthesized speech.