Towards Semantic Analysis of Conversations: A System for the Live Identification of Speakers in Meetings

Abstract

In the following article we present an application that enables online identification of who is currently speaking using a single farfield microphone in a meeting scenario. By leveraging techniques from both the field of speaker identification and speaker diarization, the system is able to recognize the current speaker after any two seconds of speech. An evaluation of the robustness of the algorithm using the AMI meeting corpus and the NIST speaker diarization development set resulted in a diarization error rate of 12.67%.