Speaker diarization using autoassociative neural networks
This paper addresses a new approach to speaker diarization using autoassociative neural networks (AANN). The speaker diarization task consists of segmenting a conversation into homogeneous segments which are then clustered into speaker classes. The proposed method uses AANN models to capture the speaker specific information from mel frequency cepstral coefficients (MFCC). The distribution capturing ability of the AANN model is utilized for segmenting the conversation and grouping each segment into one of the speaker classes. The algorithm has been tested on different databases, and the results are compared with the existing algorithms. The experimental results show that the proposed approach competes with the standard speaker diarization methods reported in the literature and it is an alternative method to the existing speaker diarization methods.
speaker diarization autoassociative neural networks mel frequency cepstral coefficients speaker segmentation speaker clustering