Nist speaker recognition evaluations sre are an ongoing series of projects conducted by nist. Robust voice activity detection for interview speech in nist. Designed as a textbook with examples and exercises at the end of each chapter, fundamentals of speaker recognition is suitable for advancedlevel students in computer science and engineering. Figure 1 shows the diagram of our rbm for pseudo ivector bvector extractor. Speaker recognition performance on the core nist sre 2010 evaluation with and without the gmmbased vad. This is evaluated on nist 2008 international speaker recognition evaluation. But system for nist 2008 speaker recognition evaluation. The ieskmagdeburg speaker detection system for the nist 2008 speaker recognition evaluation marcel katz ottovonguericke university magdeburg ieskcognitive systems katz. Speaker recognition and talkprinting sri international. System for the nist 2008 speaker recognition evaluation marcel katz ottovonguericke university magdeburg ieskcognitive systems katz. Speaker recognition in a multi speaker environment alvin f martin, mark a. Since 2008, interviewstyle speech has become an important part of the nist speaker recognition evaluations sres. 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. 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 objectives of these evaluations have been to drive forward tools and technology, measure the stateoftheart, and find the most promising algorithmic approaches in forensic speaker comparison tasks. 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. The i4u system in nist 2008 speaker recognition evaluation conference paper pdf available in acoustics, speech, and signal processing, 1988. Przybocki national institute of standards and technology gaithersburg, md 20899 usa alvin. Speech recognition prompted the speaker recognition community to try to use restricted boltzmann machines rbm for pseudo ivector extraction 810. Given that the emphasis of sre12 is on noisy and short duration test conditions, our system development focused on. National institute of standards and technology nist conducted a leaderboard style speaker recognition challenge using conversational. The 2008 nist speaker recognition evaluation results date of release. Recently we developed a series of novel techniques for speaker modeling, both in.
An overview of textindependent speaker recognition. Since 1996, national institute of standards and technology nist has carried out more than a dozen speaker recognition evaluations sre. The national institute of standards and technology conducts an ongoing series of speaker recognition evaluations sre. Dec 11, 2012 based upon the results presented using the nist 2008 speaker recognition evaluation sre dataset, we believe that, while mfdp features alone cannot compete with mfcc features, mfdp can provide complementary information that result in improved speaker verification performance when both approaches are combined in score fusion, particularly in. Iesk system marcel katz submitted systems system description discriminative classi. Nist sres speaker recognition evaluations springerlink. In recent years, nist introduces interview speech into the evaluations. Speaker recognition introduction measurement of speaker characteristics construction of speaker models decision and performance applications this lecture is based on rosenberg et al. Importance of vad in speaker verication nist sres 11 have been focusing on textindependent speaker verication over telephone channels since 1996. 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. Pdf the i4u system in nist 2008 speaker recognition. Part of the lecture notes in computer science book series lncs, volume 81. Approaches to speech recognition based on speaker recognition techniques, chapter in forthcoming gale book. Paper presented at the 2011 ieee international conference on acoustics, speech, and signal processing icassp 11, prague, czech republic.
Speaker recognition is a pattern recognition problem. Aug 06, 2008 the 2008 nist speaker recognition evaluation results date of release. Conference the reddots data collection for speaker recognition reddots project kong aik lee, anthony larcher, guangsen wang, patrick kenny, niko brummer, david van leeuwen, hagai aronowitz, marcel kockmann, carlos vaquero, bin ma, haizhou li, themos stafylakis, jahangir alam, albert swart, and javier perez, in proc. Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. Automatic speaker recognition using phase based features. A laptop with an internal microphone is centrally placed in the table of a meeting room. Unlike telephone speech, interview speech has lower signaltonoise ratio, which necessitates robust voice activity detectors vads. Since then over 50 research sites have participated in our evaluations. Original speaker recognition systems used the average output of several analog filters to perform matching, often with the aid of humans in the loop.
Within nist, the speech groups mission is to contribute to. Input audio of the unknown speaker is paired against a group of selected speakers, and if a match is found, the speakers identity is returned. Recent advances in signal processing, isbn 978953 7619411, sep 2009, intech publishing. The system consists of seven subsystems, each with different cepstral features and classifiers. 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 result is 942 pages of a good academically structured literature. Methods and the fused mfccimfcc features in the gmm based speaker recognition, book. Pdf the sri nist 2008 speaker recognition evaluation system. 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. Jfa based speaker recognition using deltaphase and mfcc. Feature vectors extracted in the feature extraction module are veri. 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. The sri nist 2008 speaker recognition evaluation system ieee.
Direct optimization of the detection cost for ivector based spoken language recognition aleksandr sizov, kong aik lee, tomi kinnunen, ieeeacm transactions on audio, speech and. The term voice recognition can refer to speaker recognition or speech recognition. The nist 2010 speaker recognition evaluation alvin f martin, craig s greenberg national institute of standards and technology, gaithersburg, maryland, usa alvin. For closing presentations from jhu 2009 workshop, see here a tutorialstyle introduction to subspace gaussian mixture models for speech recognition, microsoft research technical report msrtr2009111. Commerce departments technology administration that was created to provide standards and measurements for the u. Under funding from the national security agency, the national institute of standards and technology nist speech group began hosting yearly evaluations in 1996. Pdf the sri speaker recognition system for the 2008 nist speaker recognition evaluation sre incorporates a variety of models and. Journal duration compensation of ivector for shortduration speaker verification j. Standard approaches to automatic speaker recognition use. Since then over 70 research sites have participated in our evaluations. Utdcrss systems for 2012 nist speaker recognition evaluation.
The subdirectories v1 and so on are different ivectorbased speaker recognition recipes. The 2008 nist speaker recognition evaluation results nist. Greenberg, elliot singer, douglas reynolds, lisa mason, jaime hernandezcordero. Our primary system is a fusion of two subsystems gmmubm and gmmsvm. The national institute of standards and technology nist regularly coordinates speaker recognition technology evaluations 1, the most recent of which occurred in late 2012 2. Designed as a textbook with examples and exercises at the end of each chapter, fundamentals of speaker recognition is suitable for advancedlevel students in. Modelling, feature extraction and effects of clinical. This paper describes the performance of the i4u speaker recognition system in the nist 2008 speaker recognition evaluation. We describe the i4u primary system and report on its core test results as they were submitted, which were among the bestperforming submissions.
Level features in speaker recognition terminology is imprecise, but has traditionally meant several things in the speaker recognition community. The nist speaker recognition evaluation workshop aims to foster the continued advancement of the speaker recognition community. The latter scenario has been used in recent nist speaker recognition evaluations sres 11. Based upon the results presented using the nist 2008 speaker recognition evaluation sre dataset, we believe that, while mfdp features alone cannot compete with mfcc features, mfdp can provide complementary information that result in improved speaker verification performance when both approaches are combined in score fusion, particularly in. The i4u system in nist 2008 speaker recognition evaluation.
We also decided to test this technology for the nist ivector challenge. Speaker verification also called speaker authentication contrasts with identification, and speaker recognition differs from speaker diarisation. The sri speaker recognition system for the 2008 nist speaker recognition evaluation sre incorporates a variety of models and features, both cepstral and. Speaker recognition in a multispeaker environment alvin f martin, mark a.
It is also known as automatic speech recognition asr, computer speech recognition or speech to text stt. Svid speaker recognition system for nist sre 2012 springerlink. For each subsystem, two kinds of shorttime acoustic features plp and lpcc are adopted. The results presented within this paper using the nist 2008 speaker recognition evaluation dataset suggest that the htplda system can continue to achieve better performance than gaussian plda gplda as evaluation utterance lengths are decreased. The various technologies used to process and store voice prints include frequency estimation, hidden markov models, gaussian mixture models, pattern matching algorithms, neural networks, matrix representation, vector quantization and decision trees. Evaluations of speaker recognition systems coordinated by the national institute of standards and technology nist in gaithersburg, md, usa, 19962008. Each year new researchers in industry and universities are encouraged to participate. A study of voice activity detection techniques for nist. Collaboration between universities and industries is also welcomed. 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. Characteristics of interviewspeech in nist speaker recognition evaluation. The idiap speaker recognition evaluation system at nist.
The ieskmagdeburg speaker detection system for the nist 2008. Ppt robust voice activity detection for interview speech. But submitted three systems to nist sre 2008 evalua. Impact of prior channel information for speaker identification. Introduction 2008 nist speaker recognition evaluation test set was developed by the linguistic data consortium ldc and nist national. The sri speaker recognition system for the 2008 nist speaker recognition evaluation sre incorporates a variety of models and features, both cepstral and stylistic. Stc speaker recognition system for the nist i vector.
Optimum frequency band allocation specifically to capture speaker specific information is studied in terms of the number of subbands and spacing of center frequencies, and two new frequency band reallocations are proposed for fm based speaker recognition. Speaker recognition is the process of automatically recognizing who is speaking by using the speaker specific information included in speech waves to verify identities being claimed by people accessing systems. The system is able to identify the current speaker independent of spoken text or language with a latency of about 1. The overarching objective of the evaluations has always been to drive the technology forward, to measure the stateoftheart, and to find. The nist 2014 speaker recognition ivector machine learning.
Nist panel discussion presentation to the national academy of sciences. The api can be used to determine the identity of an unknown speaker. Speaker recognition is the identification of a person from characteristics of voices. Introduction measurement of speaker characteristics.
Nist has been coordinating speaker recognition evaluations since 1996. 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 ieskmagdeburg speaker detection system for the nist. The recipe in v1 demonstrates a standard approach using a fullcovariance gmmubm, ivectors, and a plda backend. Introduction 2008 nist speaker recognition evaluation training set part 1 was developed by ldc and nist national institute of standards. Plda based speaker recognition on short utterances qut. The task in the nist speaker recognition evaluations sre is speaker detection, i. The nist year 2008 speaker recognition evaluation plan. The 2019 nist speaker recognition evaluation cts challenge. The example in v2 replaces the gmm of the v1 recipe with a timedelay deep neural network.