Speaker diarization in a multi-speaker environment using particle swarm optimization and mutual information

The duty of speaker diarization comprises of answering the question ldquoWho spoke when?rdquo. In this paper, we present an approach comprising of PSO (particle swarm optimization) algorithm, which encodes possible segmentations of an audio record by measuring mutual information between these segments and the audio data.. This measure is used as the fitness function for the PSO. This algorithm has been tested on two actual sets of data with up to 8 speakers for speaker diarization, and has led to very good results in all test problems. The results have been compared to the same approach using genetic algorithm (GA) and the widely used DISTBIC algorithm in several practical situations, and found to be superior in most of the cases. No assumptions have been made about prior knowledge of speech signal characteristics. However, we assume that the speakers do not speak simultaneously and that we have no real-time constraints.