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Spoken language mismatch in speaker verification: An investigation with NIST-SRE and CRSS Bi-Ling corpora
2014
2014 IEEE Spoken Language Technology Workshop (SLT)
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Compensation for mismatch between acoustic conditions in automatic speaker recognition has been widely addressed in recent years. ...
However, performance degradation due to language mismatch has yet to be thoroughly addressed. In this study, we address langauge mismatch for speaker verification. ...
Using a language detector to detect the language of the test utterance and then choosing an appropriate speaker model trained on that language for scoring [6] . ...
doi:10.1109/slt.2014.7078603
dblp:conf/slt/MisraH14
fatcat:bcozgyrfyras5bys2rptrsufg4
TÜRKÇE KONUŞMACI DOĞRULAMADA DİL UYUMSUZLUĞUNUN ETKİSİ
2017
Uludağ University Journal of The Faculty of Engineering
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In this paper, effect of language mismatch between background data and evaluation data is analyzed for text-independent speaker recognition in particular for Turkish spoken language. ...
Experiments conducted on a Turkish speech database consisting of 47 male and 26 female speakers reveals that Turkish speaker recognition performance dramatically degrades in case of language mismatch between ...
The UBM for the speaker verification task is trained using English and Turkish data for the investigation of language mismatch on Turkish speaker verification. ...
doi:10.17482/uumfd.309477
fatcat:6f6su3znwbcyrny2anplatnxda
The effect of language factors for robust speaker recognition
2009
2009 IEEE International Conference on Acoustics, Speech and Signal Processing
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Based on the conventional joint factor analysis model, we enrolled in the language factors which are mean to capture the language character of each testing and training speech utterance, and compensation ...
From the results of the NIST speaker recognition evaluation in resent years, speaker recognition systems which are mainly developed based on English training data suffer the language gap problem, namely ...
Section 2 generally describes joint factor analysis model for speaker verification, and in section 3, we discuss the language factors in JFA in detail. ...
doi:10.1109/icassp.2009.4960559
dblp:conf/icassp/LuDZLW09
fatcat:wd5cv3ly3zaptf6wgvsmhqxeoe
Cross-lingual Speaker Verification with Deep Feature Learning
[article]
2017
arXiv
pre-print
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Existing speaker verification (SV) systems often suffer from performance degradation if there is any language mismatch between model training, speaker enrollment, and test. ...
In this paper, we investigate the robustness of the feature-based SV system in situations with language mismatch. ...
Conclusions This paper investigated the feature-based speaker verification approach in situations with language mismatch between training, enrollment and test. ...
arXiv:1706.07861v1
fatcat:pqfc227j7redfmnzdduded2w3u
Cross-lingual Text-independent Speaker Verification Using Unsupervised Adversarial Discriminative Domain Adaptation
2019
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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2019 pre-print arXiv:1908.01447v1 fatcat:ttycx7ucejehnpquakg3bxzkly
Speaker verification systems often degrade significantly when there is a language mismatch between training and testing data. ...
to the shift in language. ...
Language mismatch falls into two scenarios that include (i) the speaker verification system is trained on one language, but the enrollment and test data for speakers are in a second language, and (ii) ...
doi:10.1109/icassp.2019.8682259
dblp:conf/icassp/XiaHH19
fatcat:ulwrq5klbbad7dfbzdtg3b6sga
Domain-invariant I-vector Feature Extraction for PLDA Speaker Verification
2018
Odyssey 2018 The Speaker and Language Recognition Workshop
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In this paper, we introduce a domain-invariant i-vector extraction (DI-IVEC) approach to extract domain mismatch compensated out-domain i-vectors using limited in-domain (target) data for adaptation. ...
The proposed method provides at least 17.3% improvement in EER over an out-domain-only trained baseline when speaker labels are absent and a 27.2% improvement in EER when speaker labels are known. ...
Also, an additional domain mismatch can be compensated in the i-vector subspace when DI-IVEC is used in combination with DICN, thus improving the overall speaker verification performance. ...
doi:10.21437/odyssey.2018-22
dblp:conf/odyssey/RahmanHDFS18
fatcat:sl3o3lkasfa47jyknijtoqvjya
Optimization of Telephone Handset Distortion in Speaker Recognition by Discriminative Feature Design
2014
IOSR Journal of Electronics and Communication Engineering
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Effect of handset distortion was done to maximize speaker recognition performance specifically in the setting of telephone handset mismatch between training and testing as results on the 1998 NIST Speaker ...
This technique used mel-cepstral features, log spectrum and prosody based features with a nonlinear artificial neural network in designing speaker recognition features that minimize telephone handset distortion ...
A score-based handset and channel compensation method for speaker recognition systems called HNORM was presented in Reynolds, 1997b . ...
doi:10.9790/2834-09523136
fatcat:4xymka7fzfhjhb5xg7vqz4fv5i
An overview of robustness related issues in speaker recognition
2014
Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific
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For each category, we then describe the current hot topics, existing technologies, and potential research focuses in the future. I. 978-616-361-823-8 ...
We first categorize the robustness issues into three categories, including environment-related, speaker-related and application-oriented issues. ...
Research dealing with the channel mismatch of speaker verification tasks can be categorized into three directions: feature transformation [7, 19, 20] , model compensation [21, 22] and score normalization ...
doi:10.1109/apsipa.2014.7041826
dblp:conf/apsipa/ZhengJLWB14
fatcat:wjnsvr7eavambpucbcxqsftt6a
Cross-lingual speaker verification based on linear transform
2015
2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP)
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Speaker verification suffers from serious performance degradation if the enrollment and test speech are in different languages. ...
This paper proposes a linear transform approach which projects speech signals from its own language to another language so that the language mismatch between enrollment and test can be mitigated. ...
This language factor is inferred and compensated for during enrollment and test. ...
doi:10.1109/chinasip.2015.7230457
dblp:conf/chinasip/AskarWBWZ15
fatcat:nkjh2x2xkfa5lgnt7awenr5naa
Multi-variability speech database for robust speaker recognition
2011
2011 National Conference on Communications (NCC)
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The collected database is evaluated using adapted Gaussian mixture model based speaker verification system following the NIST 2003 speaker recognition evaluation protocol and gives comparable performance ...
in performance compared to the matched case whereas for language mismatch case the degradation is found to be relatively smaller. ...
on their modeling or compensation. ...
doi:10.1109/ncc.2011.5734775
fatcat:c7mqs3msafebhngoioq6jstz5m
Eigenchannel Compensation and Symmetric Score for Robust Text-Independent Speaker Verification
2008
2008 6th International Symposium on Chinese Spoken Language Processing
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The negative effect of the session variability has become more and more severe for the performance of the speaker verification system. ...
This paper discusses the eigenchannel compensation and investigates the symmetric scoring method to diminish the session variability and further enhance the performance. ...
EIGENCHANNEL COMPENSATION In eigenchannel filtering for text independent speaker verification, the GMM mean vectors for the incoming utterance can be factored in two parts. ...
doi:10.1109/chinsl.2008.ecp.92
dblp:conf/iscslp/DongZLLZW08
fatcat:qr7jch5dzfaflfzo7rad45523e
Support Vector Machine Regression for Robust Speaker Verification in Mismatching and Forensic Conditions
[chapter]
2009
Lecture Notes in Computer Science
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In this paper we propose the use of Support Vector Machine Regression (SVR) for robust speaker verification in two scenarios: i) strong mismatch in speech conditions and ii) forensic environment. ...
For the mismatching condition scenario, we use the NIST SRE 2008 core task as a highly variable environment, but with a mostly representative background set coming from past NIST evaluations. ...
Jose Juan Lucena and people from the Acoustics and Image Processing Department from Guardia Civil for their important effort in collecting data for forensic purposes. ...
doi:10.1007/978-3-642-01793-3_50
fatcat:tq3yicnaubac7fwbayony5mgjy
Investigating Deep Neural Networks for Speaker Diarization in the DIHARD Challenge
2018
2018 IEEE Spoken Language Technology Workshop (SLT)
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to compensate the mismatch in the embedding subspace. ...
In the speaker recognition task, domain adaptation works very well provided long in-domain utterance for the domain mismatch compensation training. ...
doi:10.1109/slt.2018.8639630
dblp:conf/slt/HimawanRSFK18
fatcat:5hecj7uukrdirfctzygfmbthbu
An Analysis of Transfer Learning for Domain Mismatched Text-independent Speaker Verification
2018
Odyssey 2018 The Speaker and Language Recognition Workshop
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In this paper, we present transfer learning for deep neural network based text-independent speaker verification, in the presence of a severe mismatch between the enrollment and the test data. ...
Given a pre-trained speaker embedding network developed with out-of-domain data, we explore and analyze how this pre-trained model can benefit for the in-domain speaker verification task. ...
model is essential to improve the performance for domainmismatched speaker verification. ...
doi:10.21437/odyssey.2018-26
dblp:conf/odyssey/ZhangRH18
fatcat:jo7mzr6qkfejjb6b5qmq2sdukq
A robust speaker recognition system combining factor analysis techniques
2014
2014 21th Iranian Conference on Biomedical Engineering (ICBME)
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I-Vector has been proposed and used in the field of language recognition and speaker verification. ...
JFA is a powerful tool in speaker verification task and models the speaker inter-variabilities and compensate channel and session variabilities. ...
doi:10.1109/icbme.2014.7043948
fatcat:mujdmdeu3ja2dm6dhribcoijuq
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