DISTBIC: a speaker-based segmentation for audio data indexing
Speech Communication - Special issue on accessing information in spoken audio
AANN: an alternative to GMM for pattern recognition
Neural Networks
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Review: Speaker segmentation and clustering
Signal Processing
Speaker diarization using one-class support vector machines
Speech Communication
An overview of automatic speaker diarization systems
IEEE Transactions on Audio, Speech, and Language Processing
Learning in multilayered networks used as autoassociators
IEEE Transactions on Neural Networks
Speaker diarization exploiting the eigengap criterion and cluster ensembles
IEEE Transactions on Audio, Speech, and Language Processing
Speaker diarization using low-cost wearable wireless sensors
Proceedings of the 3rd International Conference on Information and Communication Systems
Spoken keyword detection using autoassociative neural networks
International Journal of Speech Technology
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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.