The Problem of Voice Template Aging in Speaker Recognition Systems

Matveev, Yuri

doi:10.1007/978-3-319-01931-4_46

Cited by 19 publications

(11 citation statements)

References 9 publications

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“…Ageing. The effects of ageing have been analysed in several works such as [Matveev 2013] and [Kelly et al 2014]. The former work presents a brief overview on the degradation effects of ageing in automatic speaker recognition.…”

Section: Natural Disguisementioning

confidence: 99%

Voice Disguise in Automatic Speaker Recognition

Farrús

2018

ACM Comput. Surv.

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We, humans, are able to identify other people even in voice disguise conditions. However, we are not immune to all voice changes when trying to identifying people from voice. Likewise, automatic speaker recognition systems can also be deceived by voice imitation and other types of disguise. Taking into account the voice disguise classification into the combination of two different categories (deliberate/non-deliberate and electronic/non-electronic), this survey provides a literature review on the influence of voice disguise in the automatic speaker recognition task and the robustness of these systems to such voice changes. Additionally, the survey addresses existing applications dealing with voice disguise and analyses some issues for future research.

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Section: Natural Disguisementioning

confidence: 99%

Voice Disguise in Automatic Speaker Recognition

Farrús

2018

ACM Comput. Surv.

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“…Verification problems result from this change. A longitudinal voice change study found that voice pitch changes during 3-4 year time intervals deteriorates the performance of speaker identification by 40% and that the performance of a speaker identification system degrades by approximately 20% every 1-2 years [30].…”

Section: State Of Artmentioning

confidence: 99%

LOCUST - Longitudinal Corpus and Toolset for Speaker Verification

Dmitriev¹,

Kim²,

Matveeva³

et al. 2018

Interspeech 2018

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In this paper, we set forth a new longitudinal corpus and a toolset in an effort to address the influence of voice-aging on speaker verification. We have examined previous longitudinal research of agerelated voice changes as well as its applicability to real world use cases. Our findings reveal that scientists have treated agerelated voice changes as a hindrance instead of leveraging it to the advantage of the identity validator. Additionally, we found a significant dearth of publicly available corpora related to both the time span of and the number of participants in audio recordings. We also identified a significant bias toward the development of speaker recognition technologies applicable to government surveillance systems compared to speaker verification systems used in civilian IT security systems. To solve the aforementioned issues, we built an open project with the largest publicly available longitudinal speaker database, which includes 229 speakers with an average talking time exceeding 15 hours spanning across an average of 21 years per speaker. We assembled, cleaned, and normalized audio recordings and developed software tools for speech features extractions, all of which we are releasing to the public domain.

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“…The work in [10] reports that the error rate doubles when the train and the test sessions have an interval of more than a month. In [11], the author conducts a study for exploring the aging effect for data collected for an interval of four years and reports the amount of degradation in performance is gradually more for the trials having larger time interval from training.…”

Section: Introductionmentioning

confidence: 99%

Exploring Session Variability and Template Aging in Speaker Verification for Fixed Phrase Short Utterances

Das

Jelil²,

Prasanna³

2016

Interspeech 2016

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This work highlights the impact of session variability and template aging on speaker verification (SV) using fixed phrase short utterances from the RedDots database. These have been collected over a period of one year and contain a large number of sessions per speaker. Session variation has been found to have a direct influence on SV performance and its significance is even greater for the case of fixed phrase short utterances as a very small amount of speech data is involved for speaker modeling as well as testing. Similarly for a practical deployable SV system when there is large session variation involved over a period of time, the template aging of the speakers may effect the SV performance. This work attempts to address some issues related to session variability and template aging of speakers which are found for data having large session variability, that if considered can be utilized for improving the performance of an SV system.

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The Problem of Voice Template Aging in Speaker Recognition Systems

Cited by 19 publications

References 9 publications

Voice Disguise in Automatic Speaker Recognition

Voice Disguise in Automatic Speaker Recognition

LOCUST - Longitudinal Corpus and Toolset for Speaker Verification

Exploring Session Variability and Template Aging in Speaker Verification for Fixed Phrase Short Utterances

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