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Modern multi-microphone hearing aids employ spatial filtering algorithms capable of enhancing speakers from one direction whilst suppressing interfering speakers of other directions. In this context, it is useful to track moving speakers in the acoustic space by linking disjoint speech segments. Since the identity of the speakers is not known beforehand, the system must match short speech segments...
Deep learning, especially in the form of convolutional neural networks (CNNs), has triggered substantial improvements in computer vision and related fields in recent years. This progress is attributed to the shift from designing features and subsequent individual sub-systems towards learning features and recognition systems end to end from nearly unprocessed data. For speaker clustering, however,...
A Gaussian mixture model (GMM) is used in state-of-the-art i-Vector based speaker recognition systems for acoustic space division and prediction. The main purpose of such acoustic space clustering is to constrain the acoustic comparison in small regions where between-speaker differences are the main source of variability. In this study, we investigate two unsupervised discriminative approaches as...
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