Advances In Channel Compensation For SVM Speaker Recognition

Solomonoff, A.; Campbell, William M.; Boardman, Ian

doi:10.1109/icassp.2005.1415192

Cited by 212 publications

(163 citation statements)

References 4 publications

Supporting

Mentioning

163

Contrasting

Order By: Relevance

“…The problem of session variability compensation can be then addressed estimating the principal directions of channel variability in the development phase, and later canceling them out in the test phase, a process known as Nuissance Attribute Projection (NAP) (Solomonoff, Campbell, & Boardman, 2005). If we compute for every speaker in the development set the mean supervector (one per speaker), and every utterance is normalized subtracting its mean speaker vector, the resulting data set (known as within-scatter matrix) with all normalized utterances contains only session variability and no speaker information.…”

Section: High-dimensionality Spectral Systemsmentioning

confidence: 99%

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

González-Rodríguez¹

2014

loquens

View full text Add to dashboard Cite

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 ...

show abstract

Section: High-dimensionality Spectral Systemsmentioning

confidence: 99%

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

González-Rodríguez¹

2014

loquens

View full text Add to dashboard Cite

show abstract

“…Nuisance Attribute Projection (NAP) is a powerful technique traditionally used in the field of speaker recognition for compensation of channel effects regardless of its source [10,9], which are assumed to lie in a low dimensional variability subspace. In others fields like biometrics at a distance and unconstrained environments, the variability sources are mostly unknown and mixed, hence, we seek to understand to what extent variability compensation techniques as NAP are useful.…”

Section: Nuisance Attribute Projection (Nap)mentioning

confidence: 99%

Variability Compensation Using NAP for Unconstrained Face Recognition

Tomé

Vera-Rodríguez

Fiérrez

et al. 2012

Advances in Intelligent and Soft Computing

View full text Add to dashboard Cite

Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription Variability Compensation using NAP for Unconstrained Face RecognitionPedro Tome, Ruben Vera-Rodriguez, Julian Fierrez and Javier Ortega-GarcíaAbstract The variability presented in unconstrained environments represents one of the open challenges in automated face recognition systems. Several techniques have been proposed in the literature to cope with this problem, most of them tailored to compensate one specific source of variability, e.g., illumination or pose. In this paper we present a general variability compensation scheme based on the Nuisance Attribute Projection (NAP) that can be applied to compensate for any kind of variability factors that affects the face recognition performance. Our technique reduces the intra-class variability by finding a low dimensional variability subspace. This approach is assessed on a database from the NIST still face recognition challenge "The Good, the Bad, and the Ugly" (GBU). The results achieved using our implementation of a state-of-the-art system based on sparse representation are improved significantly by incorporating our variability compensation technique. These results are also compared to the GBU challenge results, highlighting the benefits of adequate variability compensation schemes in these kind of uncontrolled environments.

show abstract

“…Since the speaker related information is buried under others, raw i-vectors are not sufficiently discriminative. In order to improve the discriminative capability of i-vectors, various discriminative models have been proposed, including WCCN [6], NAP [7], LDA [8] and PLDA [9]. Among these models, PLDA is regarded as the most effective approach and delivers state-of-art performance.…”

Section: Introductionmentioning

confidence: 99%

Deep Nonlinear Metric Learning for Speaker Verification in the I-Vector Space

Feng

Xiong

Shi

2017

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYSpeaker verification is the task of determining whether two utterances represent the same person. After representing the utterances in the i-vector space, the crucial problem is only how to compute the similarity of two i-vectors. Metric learning has provided a viable solution to this problem. Until now, many metric learning algorithms have been proposed, but they are usually limited to learning a linear transformation. In this paper, we propose a nonlinear metric learning method, which learns an explicit mapping from the original space to an optimal subspace using deep Restricted Boltzmann Machine network. The proposed method is evaluated on the NIST SRE 2008 dataset. Since the proposed method has a deep learning architecture, the evaluation results show superior performance than some state-of-the-art methods.

show abstract

Advances In Channel Compensation For SVM Speaker Recognition

Cited by 212 publications

References 4 publications

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

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

Variability Compensation Using NAP for Unconstrained Face Recognition

Deep Nonlinear Metric Learning for Speaker Verification in the I-Vector Space

Contact Info

Product

Resources

About