Methods and the fused mfccimfcc features in the gmm based speaker recognition, book. 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 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. Aug 06, 2008 the 2008 nist speaker recognition evaluation results date of release. Our primary system is a fusion of two subsystems gmmubm and gmmsvm. Speaker recognition performance on the core nist sre 2010 evaluation with and without the gmmbased vad. But system for nist 2008 speaker recognition evaluation. A study of voice activity detection techniques for nist. A laptop with an internal microphone is centrally placed in the table of a meeting room. The i4u system in nist 2008 speaker recognition evaluation. The example in v2 replaces the gmm of the v1 recipe with a timedelay deep neural network. The ieskmagdeburg speaker detection system for the nist.
Speaker recognition is a pattern recognition problem. Importance of vad in speaker verication nist sres 11 have been focusing on textindependent speaker verication over telephone channels since 1996. Speaker recognition in a multispeaker environment alvin f martin, mark a. 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. 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. Commerce departments technology administration that was created to provide standards and measurements for the u. The nist 2014 speaker recognition ivector machine learning. This paper describes the performance of the i4u speaker recognition system in the nist 2008 speaker recognition evaluation. This is evaluated on nist 2008 international speaker recognition evaluation. National institute of standards and technology nist conducted a leaderboard style speaker recognition challenge using conversational.
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. 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. 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 term voice recognition can refer to speaker recognition or speech recognition. Przybocki national institute of standards and technology gaithersburg, md 20899 usa alvin. 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 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. Journal duration compensation of ivector for shortduration speaker verification j. Under funding from the national security agency, the national institute of standards and technology nist speech group began hosting yearly evaluations in 1996. Unlike telephone speech, interview speech has lower signaltonoise ratio, which necessitates robust voice activity detectors vads. The system is able to identify the current speaker independent of spoken text or language with a latency of about 1. Since then over 70 research sites have participated in our evaluations. The overarching objective of the evaluations has always been to drive the technology forward, to measure the stateoftheart, and to find. The api can be used to determine the identity of an unknown speaker.
Each year new researchers in industry and universities are encouraged to participate. We describe the i4u primary system and report on its core test results as they were submitted, which were among the bestperforming submissions. The system consists of seven subsystems, each with different cepstral features and classifiers. Part of the lecture notes in computer science book series lncs, volume 81. Figure 1 shows the diagram of our rbm for pseudo ivector bvector extractor.
Level features in speaker recognition terminology is imprecise, but has traditionally meant several things in the speaker recognition community. Speaker recognition in a multi speaker environment alvin f martin, mark a. 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. The nist speaker recognition evaluation workshop aims to foster the continued advancement of the speaker recognition community. The recipe in v1 demonstrates a standard approach using a fullcovariance gmmubm, ivectors, and a plda backend. The sri nist 2008 speaker recognition evaluation system ieee. Pdf the sri speaker recognition system for the 2008 nist speaker recognition evaluation sre incorporates a variety of models and. Since then over 50 research sites have participated in our evaluations. The sri speaker recognition system for the 2008 nist speaker recognition evaluation sre incorporates a variety of models and features, both cepstral and stylistic.
We also decided to test this technology for the nist ivector challenge. Collaboration between universities and industries is also welcomed. 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. Speaker verification also called speaker authentication contrasts with identification, and speaker recognition differs from speaker diarisation. Nist sres speaker recognition evaluations springerlink. In recent years, nist introduces interview speech into the evaluations. Stc speaker recognition system for the nist i vector. The 2008 nist speaker recognition evaluation results date of release. Introduction 2008 nist speaker recognition evaluation test set was developed by the linguistic data consortium ldc and nist national. Modelling, feature extraction and effects of clinical. But submitted three systems to nist sre 2008 evalua.
The ieskmagdeburg speaker detection system for the nist 2008. Feature vectors extracted in the feature extraction module are veri. Given that the emphasis of sre12 is on noisy and short duration test conditions, our system development focused on. 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. Designed as a textbook with examples and exercises at the end of each chapter, fundamentals of speaker recognition is suitable for advancedlevel students in.
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. Utdcrss systems for 2012 nist speaker recognition evaluation. Impact of prior channel information for speaker identification. Speaker recognition and talkprinting sri international. Evaluations of speaker recognition systems coordinated by the national institute of standards and technology nist in gaithersburg, md, usa, 19962008. Introduction 2008 nist speaker recognition evaluation training set part 1 was developed by ldc and nist national institute of standards. 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 nist year 2008 speaker recognition evaluation plan. 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. It is also known as automatic speech recognition asr, computer speech recognition or speech to text stt. For each subsystem, two kinds of shorttime acoustic features plp and lpcc are adopted.
The 2008 nist speaker recognition evaluation results nist. The 2019 nist speaker recognition evaluation cts challenge. The result is 942 pages of a good academically structured literature. Original speaker recognition systems used the average output of several analog filters to perform matching, often with the aid of humans in the loop. Automatic speaker recognition using phase based features. Within nist, the speech groups mission is to contribute to. Paper presented at the 2011 ieee international conference on acoustics, speech, and signal processing icassp 11, prague, czech republic. The ieskmagdeburg speaker detection system for the nist 2008 speaker recognition evaluation marcel katz ottovonguericke university magdeburg ieskcognitive systems katz. 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. Since 2008, interviewstyle speech has become an important part of the nist speaker recognition evaluations sres. The latter scenario has been used in recent nist speaker recognition evaluations sres 11. 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. Introduction measurement of speaker characteristics. 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.
Nist panel discussion presentation to the national academy of sciences. It contains 640 hours of multilingual telephone speech and english interview speech along with timealigned transcripts and other materials used as training data in the 2008 nist speaker recognition. Speech recognition prompted the speaker recognition community to try to use restricted boltzmann machines rbm for pseudo ivector extraction 810. Standard approaches to automatic speaker recognition use. Iesk system marcel katz submitted systems system description discriminative classi. Robust voice activity detection for interview speech in nist. 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. Speaker recognition introduction measurement of speaker characteristics construction of speaker models decision and performance applications this lecture is based on rosenberg et al. Nist speaker recognition evaluations sre are an ongoing series of projects conducted by nist. Svid speaker recognition system for nist sre 2012 springerlink. Greenberg, elliot singer, douglas reynolds, lisa mason, jaime hernandezcordero.
Recently we developed a series of novel techniques for speaker modeling, both in. 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. Plda based speaker recognition on short utterances qut. The nist series of speaker recognition evaluations sres have, since 1996, evaluated automatic systems for speaker recognition.
The idiap speaker recognition evaluation system at nist. Jfa based speaker recognition using deltaphase and mfcc. Approaches to speech recognition based on speaker recognition techniques, chapter in forthcoming gale book. Features that span temporal regions longer than a typical frame 10. 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. Nist has been coordinating speaker recognition evaluations since 1996. Ppt robust voice activity detection for interview speech. Pdf the sri nist 2008 speaker recognition evaluation system. Since 1996, national institute of standards and technology nist has carried out more than a dozen speaker recognition evaluations sre. 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 nist 2010 speaker recognition evaluation alvin f martin, craig s greenberg national institute of standards and technology, gaithersburg, maryland, usa alvin. The national institute of standards and technology conducts an ongoing series of speaker recognition evaluations sre. An overview of textindependent speaker recognition.
866 1360 975 635 1091 360 424 68 144 1250 1009 1313 1552 716 210 1313 1314 172 1443 1469 845 545 268 823 1556 289 856 619 106 661 929 165 1455 590 534 880 206 1202 1427 770 924 317 281 1486 1248 123 806 1341 286