Speaker segmentation and clustering in meetings
Qin Jin, Tanja Schultz · 2004
This paper describes the issue of automatic speaker segmentation and clustering for natural, multi-speaker meeting conversations. Two systems were developed and evaluated in the NIST RT-04S Meeting Recognition Evaluation, the Multiple Distant Microphone (MDM) system and the Individual Headset Microphone (IHM) system. The MDM system achieved a speaker diarization performance of 28.17%. This system also aims to provide automatic speech segments and speaker grouping information for speech recognition, a necessary prerequisite for subsequent audio processing. A 44.5 % word error rate was achieved for speech recognition. The IHM system is based on the short-time crosscorrelation of all personal channel pairs. It requires no prior training and executes in one fifth real time on modern architectures. A 35.7 % word error rate was achieved for speech recognition when segmentation was provided by this system. 1.