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

Hongcui WANG; Shanshan LIU; Di JIN; Lantian LI; Jianwu DANG

doi:10.1587/transinf.2018EDP7356

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

Hongcui WANG, Shanshan LIU, Di JIN, Lantian LI, Jianwu DANG

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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.

Publication: IEICE TRANSACTIONS on Information 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

Authors

Hongcui WANG
  Tianjin University,Zhejiang University of Water Resouces and Electric Power
Shanshan LIU
  Tianjin University
Di JIN
  Tianjin University
Lantian LI
  Tsinghua University
Jianwu DANG
  Tianjin University,Japan Advanced Institute of Science and Technology

Keyword

community detection, i-vector space, manifold learning, speaker clustering, speaker graph content

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Hongcui WANG, Shanshan LIU, Di JIN, Lantian LI, Jianwu DANG, "Scalable Community Identification with Manifold Learning on Speaker I-Vector Space" in IEICE TRANSACTIONS on Information, vol. E102-D, no. 10, pp. 2004-2012, October 2019, doi: 10.1587/transinf.2018EDP7356.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7356/_p

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@ARTICLE{e102-d_10_2004,
author={Hongcui WANG, Shanshan LIU, Di JIN, Lantian LI, Jianwu DANG, },
journal={IEICE TRANSACTIONS on Information},
title={Scalable Community Identification with Manifold Learning on Speaker I-Vector Space},
year={2019},
volume={E102-D},
number={10},
pages={2004-2012},
abstract={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.},
keywords={},
doi={10.1587/transinf.2018EDP7356},
ISSN={1745-1361},
month={October},}

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TY - JOUR
TI - Scalable Community Identification with Manifold Learning on Speaker I-Vector Space
T2 - IEICE TRANSACTIONS on Information
SP - 2004
EP - 2012
AU - Hongcui WANG
AU - Shanshan LIU
AU - Di JIN
AU - Lantian LI
AU - Jianwu DANG
PY - 2019
DO - 10.1587/transinf.2018EDP7356
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2019
AB - 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.
ER -