Scalable Community Identification with Manifold Learning on Speaker I-Vector Space

Hongcui WANG  Shanshan LIU  Di JIN  Lantian LI  Jianwu DANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.10   pp.2004-2012
Publication Date: 2019/10/01
Publicized: 2019/07/10
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDP7356
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
community detection,  i-vector space,  manifold learning,  speaker clustering,  speaker graph content,  

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Recognizing the different segments of speech belonging to the same speaker is an important speech analysis task in various applications. Recent works have shown that there was an underlying manifold on which speaker utterances live in the model-parameter space. However, most speaker clustering methods work on the Euclidean space, and hence often fail to discover the intrinsic geometrical structure of the data space and fail to use such kind of features. For this problem, we consider to convert the speaker i-vector representation of utterances in the Euclidean space into a network structure constructed based on the local (k) nearest neighbor relationship of these signals. We then propose an efficient community detection model on the speaker content network for clustering signals. The new model is based on the probabilistic community memberships, and is further refined with the idea that: if two connected nodes have a high similarity, their community membership distributions in the model should be made close. This refinement enhances the local invariance assumption, and thus better respects the structure of the underlying manifold than the existing community detection methods. Some experiments are conducted on graphs built from two Chinese speech databases and a NIST 2008 Speaker Recognition Evaluations (SREs). The results provided the insight into the structure of the speakers present in the data and also confirmed the effectiveness of the proposed new method. Our new method yields better performance compared to with the other state-of-the-art clustering algorithms. Metrics for constructing speaker content graph is also discussed.