Text-dependent speaker verification: Classifiers, databases and RSR2015

Larcher, Anthony; Lee, Kong Aik; Ma, Bin; Li, Haizhou

doi:10.1016/j.specom.2014.03.001

Cited by 232 publications

(260 citation statements)

References 85 publications

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“…Experimental carried out on the RSR database (Larcher et al, 2014) indicate superior performance of the JFA approach (Dey et al, 2016a). Even though JFA explicitly models phonetic content for text-dependent task, sequence information for the content variability is still ignored.…”

Section: Content or Linguistic Information Is Relevant To Text-dependmentioning

confidence: 99%

“…The model was used to obtain speaker-content projected i-vectors to be used in the DTW algorithm. These techniques are evaluated on the fixed-phrase parts of the RedDots (Lee et al, 2015) and RSR (Larcher et al, 2014) corpora, both designed for text-dependent SV.…”

Section: Content or Linguistic Information Is Relevant To Text-dependmentioning

confidence: 99%

“…Estimating the i-vector representation of an utterance involves computing the zeroth and first order statistics with respect to the UBM (Larcher et al, 2014;Dey et al, 2016a). In a simplified PLDA model (Romero and Wilson, 2011), an i-vector (w) can be decomposed into speaker factors (h) and channel effects as follows:…”

Section: I-vector Systemmentioning

confidence: 99%

“…For a text-dependent scenario, the outcome of the task is linked to identifying content and speaker. This motivates the use of speaker-content classes for PLDA training (Dey et al, 2016a;Larcher et al, 2014). Besides labeling content as whole phrases, phone classes can be obtained from a forced alignment of the data against given transcripts as well.…”

Section: Plda Projection Featuresmentioning

confidence: 99%

See 3 more Smart Citations

Template-matching for text-dependent speaker verification

Dey

Motlíček

Madikeri

et al. 2017

Speech Communication

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Section: Content or Linguistic Information Is Relevant To Text-dependmentioning

confidence: 99%

Section: Content or Linguistic Information Is Relevant To Text-dependmentioning

confidence: 99%

Section: I-vector Systemmentioning

confidence: 99%

Section: Plda Projection Featuresmentioning

confidence: 99%

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Template-matching for text-dependent speaker verification

Dey

Motlíček

Madikeri

et al. 2017

Speech Communication

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show abstract

“…Interestingly, the organizers have madepubliclyavailabilitytheRSR2015database,including 150 hours of data recorded from 300 speakers in mobile environments, which allow text-dependent system design and evaluation in different configurations, and comparison of results with other systems using the same database (Larcher, Lee, Ma, & Li, 2014).…”

Section: Text-dependent and Text-independent Systemsmentioning

confidence: 99%

Evaluating Automatic Speaker Recognition systems: An overview of the NIST Speaker Recognition Evaluations (1996-2014)

González-Rodríguez¹

2014

loquens

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Automatic Speaker Recognition systems show interesting properties, such as speed of processing or repeatability of results, in contrast to speaker recognition by humans. But they will be usable just if they are reliable. Testability, or the ability to extensively evaluate the goodness of the speaker detector decisions, becomes then critical. In the last 20 years, the US National Institute of Standards and Technology (NIST) has organized, providing the proper speech data and evaluation protocols, a series of text-independent Speaker Recognition Evaluations (SRE). Those evaluations have become not just a periodical benchmark test, but also a meeting point of a collaborative community of scientists that have been deeply involved in the cycle of evaluations, allowing tremendous progress in a specially complex task where the speaker information is spread across different information levels (acoustic, prosodic, linguistic…) and is strongly affected by speaker intrinsic and extrinsic variability factors. In this paper, we outline how the evaluations progressively challenged the technology including new speaking conditions and sources of variability, and how the scientific community gave answers to those demands. Finally, NIST SREs will be shown to be not free of inconveniences, and future challenges to speaker recognition assessment will also be discussed. NIST de reconocimiento de locutor (1996NIST de reconocimiento de locutor ( -2014.-Los sistemas automáticos de reconocimiento de locutor son críticos para la organización, etiquetado, gestión y toma de decisiones sobre grandes bases de datos de voces de diferentes locutores. Con el fin de procesar eficientemente tales cantidades de información de voz, necesitamos sistemas muy rápidos y, al no estar libre de errores, lo suficientemente fiables. Los sistemas actuales son órdenes de magnitud más rápidos que tiempo real, permitiendo tomar decisiones automáticas instantáneas sobre enormes cantidades de conversaciones. Pero tal vez la característica más interesante de un sistema automático es la posibilidad de ser analizado en detalle, ya que su rendimiento y fiabilidad puede ser evaluada de manera ciega sobre cantidades enormes de datos en una gran diversidad de condiciones. En los últimos 20 años, el Instituto Nacional de Estándares y Tecnología (NIST) de EE. UU. ha organizado, proporcionando los datos de voz y protocolos de evaluación adecuada, una serie de evaluaciones de reconocimiento de locutor independiente del texto. Esas evaluaciones se han convertido no sólo en una prueba comparativa periódica, sino también en punto de encuentro de una comunidad colaborativa de científicos que han estado profundamente involucrados en el ciclo de evaluaciones, lo que ha permitido un enorme progreso en una tarea especialmente compleja en la que la información individualizadora del locutor se encuentra dispersa en diferentes niveles de información (acústica, prosódica, lingüística...) y está fuertemente afectada por factores de variabilidad intrínsecos y extrínsecos al ...

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Covariance Based Deep Feature for Text-Dependent Speaker Verification

Wang

Dinkel

et al. 2018

Lecture Notes in Computer Science

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Text-dependent speaker verification: Classifiers, databases and RSR2015

Cited by 232 publications

References 85 publications

Template-matching for text-dependent speaker verification

Template-matching for text-dependent speaker verification

Evaluating Automatic Speaker Recognition systems: An overview of the NIST Speaker Recognition Evaluations (1996-2014)

Covariance Based Deep Feature for Text-Dependent Speaker Verification

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