The 2008 nist speaker recognition evaluation results date of release. It contains 942 hours of multilingual telephone speech and english interview speech along with transcripts and other materials used as test data in the 2008 nist speaker recognition. The nist year 2008 speaker recognition evaluation plan. Analysis of the utility of classical and novel speech. The iir nist sre 2008 and 2010 summed channel speaker recognition systems. The ieskmagdeburg speaker detection system for the nist 2008. Evaluated on multilingual trial condition, the proposed solution demonstrated over 10% eer and % minimum dcf relative improvement on nist 2008 speaker recognition evaluation as well as 12. Speaker recognition in a multispeaker environment nist. For each subsystem, two kinds of shorttime acoustic features plp and lpcc are adopted. Robust voice activity detection for interview speech in nist speaker recognition evaluation manwai mak and honbill yu center for signal processing, department of electronic and information engineering, the hong kong polytechnic university abstract the introduction of interview speech in recent nist speaker recognition evaluations sres has. Speaker recognition also uses the same features, most of the same frontend processing, and classification techniques as is done in speech recognition. A description of the svid speaker recognition system is presented.
Automatic speaker recognition is the use of a machine to recognize a person from a spoken phrase. Performance across telephone and room microphone channels alvin f. In this paper, the fusion of two speaker recognition subsystems, one based on frequency modulation fm and another on mfcc features, is reported. We discuss the multi speaker tasks of detection, tracking, and segmentation of speakers as included in recent nist speaker recognition evaluations. The proposed approach seeks robustness to situations where a proper background database is reduced or not present, a situation typical in forensic cases which has. Stc speaker recognition system for the nist i vector. The year 2008 speaker recognition evaluation is limited to the broadly defined task of speaker detection. The iir nist sre 2008 and 2010 summed channel speaker. This paper describes the performance of the i4u speaker recognition system in the nist 2008 speaker recognition evaluation. Evaluation of a speaker identification system with and. Robust voice activity detection for interview speech in nist speaker recognition evaluation manwai mak and honbill yu center for signal processing, department of electronic and information engineering, the hong kong polytechnic university abstractthe introduction of interview speech in recent nist.
Evaluated on the recent nist 2008 and 2010 speaker recognition evaluations sre, the proposed technique demonstrated improvements of up to 31% in minimum dcf and eer under mismatched and sparselyresourced conditions. For each trial, the system to be evaluated needed to decide whether speech of the target speaker. Summary thanks to the commitment of researchers and the support of nsa and nist, speaker recognition will continue to evolve as communication and computing technology advance. The database is available at, and its sources are multilingual telephone and microphone speech of native and bilingual english interview speakers. Bayesian speech and language processing by shinji watanabe.
This paper presents an extension of our previous work which proposes a new speaker representation for speaker verification. Martin and others published nist 2008 speaker recognition evaluation. The motivation for their fusion was to improve the recognition accuracy across different types of channel variations, since the two features are believed to contain complementary information. Pdf the sri speaker recognition system for the 2010 nist speaker. The databases employed were timit, sitw, and nist 2008. This was the focus of nist speaker recognition evaluation sre in 20052008 martin and greenberg, 2009.
In our ubmgmm based speaker recognition system kinnunen and lib 2010, the universal background model ubm is trained with data obtained from a set of 325 speakers. A comprehensive textbook, fundamentals of speaker recognition is an in depth source for up to date details on the theory and practice. Nist evaluations in speaker diarization the national institute of standards and technology national institute for standards and technology, 2006 nist is an agency of the u. Sourcenormalized lda for robust speaker recognition using i. A range of statistical models is detailed, from hidden markov models to gaussian mixture models, ngram models and latent topic models, along with applications including automatic speech recognition, speaker verification, and information retrieval. An overview of textindependent speaker recognition. The nist 2014 speaker recognition ivector machine learning. An improved ivector extraction algorithm for speaker. Sris 2008 nist sre system incorporated both approaches, as. Introduction 2008 nist speaker recognition evaluation training set part 1 was developed by ldc and nist national institute of standards. Pdf but system for nist 2008 speaker recognition evaluation.
Development of the primary crim system for the nist 2008. The system consists of seven subsystems, each with different cepstral. An ivector is a compact representation of a speakers utterance extracted from a total variability subspace. The 2019 nist audiovisual speaker recognition evaluation. Stc speaker recognition system for the nist ivector challenge sergey novoselov1, timur pekhovsky1,2, konstantin simonchik1,2 1 department of speaker verification and identification, speech technology center ltd. In speaker recognition, a robust recognition method is essential. The system consists of seven subsystems, each with different cepstral features and classifiers. The main problem of this task is the small amount of microphone data at our disposal to extract the ivector features. Nist speaker recognition evaluation chronicles nist. Data dependency on measurement uncertainties in speaker. Government agencies, such as department of defense, department of justice, and intelligence advanced research projects activity iarpa, to build a forum for the advancement of speaker recognition technology through evaluationdriven research. Frontend factor analysis for speaker verification ieee. The example in v2 replaces the gmm of the v1 recipe with a timedelay deep neural network.
Since then over 50 research sites have participated in our evaluations. Nist conducted the most recent in an ongoing series of speaker recognition evaluations sre the 2019 nist speaker recognition evaluation cts challenge. The nist 2010 speaker recognition evaluation alvin f martin, craig s greenberg national institute of standards and technology, gaithersburg, maryland, usa alvin. Our primary system is a fusion of two subsystems gmmubm and gmmsvm. The objective of learning the metric is to ensure that the knearest neighbors that belong to the same speaker are clustered together, while impostors are moved away by a large margin. Nist sres speaker recognition evaluations springerlink. Greenberg national institute of standards and technology, gaithersburg, maryland, usa alvin. In this work, we present the application of the fusion of tdnn and lstmp to the ivector speaker. The sri nist 2008 speaker recognition evaluation system ieee. An emerging technology, speaker recognition is becoming wellknown for providing voice authentication over the telephone for helpdesks. Commerce departments technology administration that was created to provide standards and measurements for the u. Tul system for the nist 2008 speaker recognition evaluation.
Each test consisted of a sequence of trials, where each trial consisted of a target speaker, defined by the training data provided, and a test segment. Introduction 2008 nist speaker recognition evaluation test set was developed by the linguistic data consortium ldc and nist national. Robust voice activity detection for interview speech in nist. We describe the 2008 nist speaker recognition evaluation, including the speech data used, the test conditions included, the participants, and some of the performance results obtained.
This paper proposes a speaker verification method that is based on the timedelay neural network tdnn and long shortterm memory with recurrent project layer lstmp model for the speaker modeling problem in speaker verification. Nist has been coordinating speaker recognition evaluations since 1996. Gmmsvm kernel with a bhattacharyyabased distance for speaker recognition changhuai you, kong aik lee, and haizhou li, ieee trans. Their determination will help to further develop the technology into a page 2 of 166. Text independent speaker verification experiments using gaussian mixture models gmm are conducted on a subset of the nist 2008 speaker recognition evaluation sre nist 2008. Two decades of speaker recognition evaluation at the national. Evaluation of a speaker identification system with and without fusion. Indeed, this limited quantity does not allow a robust estimation of the total variability covariance matrix. Ldc partners with nists multimodal information group and retrieval group to provide training, development and test data for research areas that include speech recognition, language recognition, machine translation, cross. The i4u system in nist 2008 speaker recognition evaluation ieee. It was found that the mfccbased subsystem outperformed.
Aug 06, 2008 the 2008 nist speaker recognition evaluation results date of release. This evaluation was distinguished by including as part of the required test condition interview type speech as well as conversational telephone speech, and speech recorded over microphone. Przybocki national institute of standards and technology gaithersburg, md 20899 usa alvin. Speaker segmentation experiments are carried out on 36 summed channel conversations in the nist 2004 speaker recognition evaluation. See book chapter for more complete table and references.
Development of the primary crim system for the nist 2008 speaker recognition evaluation. Frontend factor analysis for speaker verification abstract. Speaker recognition in a multi speaker environment alvin f martin, mark a. The algorithm incorporated elements from lpts 2008 pro. The ieskmagdeburg speaker detection system for the nist 2008 speaker recognition evaluation marcel katz ottovonguericke university magdeburg ieskcognitive systems katz. Pdf the sri nist 2010 speaker recognition evaluation system. An svm kernel with gmmsupervector based on the bhattacharyya distance for speaker recognition. Introduction the goal of this paper is to present a consolidated version of butsystem description with resultsobtained on sre2006 and 2008 data, and todiscuss performances ofindividual systems as well as their fusion. The 2010 evaluation sre10 also included a test of human assisted speaker recognition hasr, in which systems based, in whole or in part, on human expertise were evaluated. We consider how performance for the two speaker detection task is related to that for the corresponding one speaker. Among the tests given in the 2008 nist sre, ther e was a single core test formed by short2 of. Training, evaluation and supplemental data from 2008 sre are available in the ldc catalog. The goal of the nist speaker recognition evaluation sre series is to contribute to the direction of research efforts and the calibration of technical capabilities of text independent speaker recognition. Sourcenormalisedandweighted lda for robust speaker.
This system was developed for submission to the nist sre 2012. In this modeling, a new lowdimensional speaker and channeldependent space is defined using a simple factor analysis. Greenberg, nist 2008 speaker recognition evaluation. A speaker verification method based on tdnnlstmp springerlink. The 2008 nist speaker recognition evaluation results nist. The nist 2008 metrics for machine translation challenge overview, methodology, metrics, and results. The sri nist 2008 speaker recognition evaluation system. Evaluation of a fused fm and cepstralbased speaker. The nist series of speaker recognition evaluations sres have, since 1996, evaluated automatic systems for speaker recognition. In this paper we propose the use of support vector machine regression svr for robust speaker verification in two scenarios. We reported results on the male english trials of the core condition of the nist 2008 speaker recognition evaluation sres dataset. Pdf the i4u system in nist 2008 speaker recognition. The nist speaker recognition evaluation conversational telephone speech cts challenge 2019 was an open evaluation for the task of speaker verification in challenging conditions. Mar 20, 2019 in speaker recognition, a robust recognition method is essential.
Jun 27, 2015 this new algorithm contributes to better modelling of session variability in the total factor space. A plda approach for language and text independent speaker. Fundamentals of speaker recognition homayoon beigi on. Pdf the sri nist 2008 speaker recognition evaluation system. Wednesday, august 6, 2008 the goal of the nist speaker recognition evaluation sre series is to contribute to the direction of research efforts and the calibration of technical capabilities of text independent speaker recognition. This process is called textdependent speaker verification as opposed to. The overarching objective of the evaluations has always been to drive the technology forward. In the 2008 nist sre, generally speaking, the total number of target scores was about 20,000 and the total number of nontarget scores was about 80,000 2, 3. Support vector machine regression for robust speaker.
Pdf the sri nist 2008 speaker recognition evaluation. We highlight the improvements made to specific subsystems and analyze the performance of various subsystem combinations in different data conditions. An ivector extractor suitable for speaker recognition. Automatic speaker recognition using phase based features. This publications database includes many of the most recent publications of the national institute of standards and technology nist. The recipe in v1 demonstrates a standard approach using a fullcovariance gmmubm, ivectors, and a plda backend. But system for nist 2008 speaker recognition evaluation.
But submitted three systems to nist sre 2008 evalua. Experiments conducted on the nist 2008 and yoho databases show improved performance compared to speaker verification system, where no learned metric is used. Much use of highlevel featured in nist speaker recognition evaluations sre in following. The sri speaker recognition system for the 2008 nist speaker recognition evaluation sre incorporates a variety of models and features, both cepstral and stylistic. The recent development of the ivector framework for speaker recognition has set a new performance standard in the research field. Each test in the nist sre has consisted of a sequence of trials, where each trial consists of a model speaker, based on the training data provided, and a test speech segment. The sri speaker recognition system for the 2008 nist speaker recognition evaluation sre incorporates a variety of models and features, both cepstral and. Cosine distance metric learning for speaker verification. Pdf ifly system for the nist 2008 speaker recognition. Since its founding in 1992, ldc has worked with the national institute of standards and technology nist on a series of ongoing human language technology evaluations. We converted the sampling frequency from the original 8 to 16 khz, and 120 english only microphone channel speakers were selected. The description of ifly system submitted for nist 2008 speaker recognition evaluation sre, which has achieved excellent performance in the 2008 sre evaluation, is presented in this paper.
The nist speaker recognition evaluation overview methodology, systems, results, perspective. Although current textindependent speaker recognition systems are considered to be independent of the language being spoken, their performance will be affected in multilingual trial condition. Uncertainties of measures in speaker recognition evaluation. The subdirectories v1 and so on are different ivectorbased speaker recognition recipes. Developing fm based automatic speaker recognition system to complement conventional systems thiruvaran, tharmarajah, ambikairajah, eliathamby, epps, julien on. The nist 2014 speaker recognition ivector machine learning challenge craig s. The multimodal information groups mig video analytics program includes several activities contributing to the development of technologies that extract speaker and language recognition. In the 2008 nist sre, each test generally included about 20,000 target. Part of the lecture notes in computer science book series lncs, volume 81. Evaluations of speaker recognition systems coordinated by the national institute of standards and technology nist in gaithersburg, md, usa, 1996 2008. The i4u system in nist 2008 speaker recognition evaluation.
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