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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

A framework for speaker retrieval and identification through unsupervised learning

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Author(s):
Campos, Victor de Abreu [1] ; Guimaraes Pedronette, Daniel Carlos [1]
Total Authors: 2
Affiliation:
[1] State Univ Sao Paulo, UNESP, Dept Stat Appl Math & Comp, Rio Claro - Brazil
Total Affiliations: 1
Document type: Journal article
Source: COMPUTER SPEECH AND LANGUAGE; v. 58, p. 153-174, NOV 2019.
Web of Science Citations: 0
Abstract

Speaker recognition is a task of remarkable relevance, with applications in diversified domains. Recently, mainly due to the facilities in audio-visual content acquisition, the capacity of analyzing growing datasets independent of labeled data has become a crucial advantage. This paper presents a speaker recognition approach based on recent unsupervised learning methods, which do not require any labeled data or user intervention. The approach is organized in terms of a framework which exploits a rank-based formulation. The similarity information defined by speaker modeling techniques is encoded in ranked lists, which are used as input by the unsupervised learning algorithms. Vector quantization, Gaussian mixture models and i-vectors are employed as modeling techniques, while the algorithms RL-Sim and ReckNN are used for unsupervised learning tasks. The framework was experimentally evaluated on query-by-example speaker retrieval and speaker identification tasks, both on clean and noisy speech recordings. An experimental evaluation was conducted on three public datasets, different languages, and recordings conditions. Effectiveness gains up to +56% on retrieval measures were obtained through the use of unsupervised learning algorithms over traditional speaker recognition techniques. (C) 2019 Elsevier Ltd. All rights reserved. (AU)

FAPESP's process: 17/25908-6 - Weakly supervised learning for compressed video analysis on retrieval and classification tasks for visual alert
Grantee:João Paulo Papa
Support type: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 15/07934-4 - Automatic speaker identification using unsupervised learning
Grantee:Victor de Abreu Campos
Support type: Scholarships in Brazil - Master
FAPESP's process: 18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks
Grantee:Daniel Carlos Guimarães Pedronette
Support type: Research Grants - Young Investigators Grants - Phase 2