Adaptive and online speaker diarization for meeting data

Speaker diarization aims to determine `who spoke when' in a given audio stream. Different applications, such as document structuring or information retrieval have led to the exploration of speaker diarization in many different domains, from broadcast news to lectures, phone conversations and meetings. Almost all current diarization systems are offline and ill-suited to the growing need for online or real-time diarization, stemming from the increasing popularity of powerful, mobile smart devices. While a small number of such systems have been reported, truly online diarization systems for challenging and highly spontaneous meeting data are lacking. This paper reports our work to develop an adaptive and online diarization system using the NIST Rich Transcription meetings corpora. While not dissimilar to those previously reported for less challenging domains, high diarization error rates illustrate the challenge ahead and lead to some ideas to improve performance through future research.