Speaker diarization system using MKMFCC parameterization and WLI-fuzzy clustering

Speaker diarization is the process of determining “who speak when?” with appropriate speaker labels with respect to the time regions where they spoke. Accordingly, in the previous work, a model based speaker diarization using the tangential weighted Mel frequency cepstral coefficients as the feature parameter for the voice activity detection and Lion optimization algorithm for the clustering of the audio streams into speaker group was performed. In this paper, speaker diarization system is proposed using multiple kernel weighted Mel frequency cepstral coefficient (MKMFCC) parameterization and Wu-and-Li Index (WLI)-fuzzy clustering. First, a MKMFCC which utilizes the multiple kernels like the tangential and exponential for weighting the MFCC’s is proposed for the feature parameterization. Second, a clustering algorithm called the WLI-Fuzzy clustering is proposed for grouping the segments of the same speaker groups. The experimentation of the proposed speaker diarization system is carried out over the publically available ELSDSR corpus data set having the audio signal with seven different speakers. The performance evaluation of the proposed speaker diarization system is analysed using the measures such as diarization error rate, F-measure and false alarm rate. The results show that the proposed speaker diarization system proved better for tracking the active speakers from multiple speakers with improved tracking accuracy.