In this paper, we propose to use eigenvoice coefficients as features for speaker recognition. Eigenvoice and vector taylor series vts are good models for speaker differences and environmental variations separately. The identity toolbox provides tools that implement both the conventional gmmubm and stateoftheart ivector based speaker recognition strategies. Select the testing console in the region where you created your resource. In this thesis, we concentrate ourselves on speaker recognition systems srs. Speaker recognition using evectors acm digital library. A possible solution is the eigenvoice approach, in which client and test speaker models are confined to a lowdimensional linear subspace obtained previously. However, speaker and environmental variation always coexist in realworld speech. It can be used for authentication, surveillance, forensic speaker recognition and a number of related activities. Dumouchel abstractwe compare two approaches to the problem of session variability in gmmbased speaker veri. The approach constrains the adapted model to be a linear combination. Fundamentals of speaker recognition homayoon beigi on. Speaker modeling technique with sparse training data is an active branch of robust speaker recognition research.
Speechpros stateoftheart speaker recognition technology proved its excellence in law enforcements all over the world. Speaker recognition in a multi speaker environment alvin f martin, mark a. Combining the eigenvoice assumption with emap gives eigenvoice map 8. View speaker recognition research papers on academia. Dimensionality reduction techniques are al ready widely used in speech recognition. Recognizing the speaker can simplify the task of translating speech in systems that have been trained on specific voices or it can be used to. Hmm based xvector clustering for speaker diarization, in proceedings of interspeech 2019, 2019. Speaker verification apis serve as an intelligent tool to help verify speakers using both their voice and speech passphrases. Recognition of signals containing contributions from multiple sources continues to pose a significant problem for.
We would like to revisit a simple fast adaptation technique called reference speaker weighting rsw. Each eigenvoice models a direction of inter speaker variability. A new eigenvoice approach to speaker adaptation chihhsien huanga, jentzung chiena and hsinmin wangb a department of computer science and information engineering, cheng kung university, tainan b institute of information science, academia sinica, taipei email. A read is counted each time someone views a publication summary such as the title, abstract, and list of authors, clicks on a figure, or views or downloads the fulltext. In such applications, the voice samples are most probably. Rsw is similar to eigenvoice ev adaptation, and simply requires the model of a new speaker to lie on the span of a set of reference speaker vectors. It is no doubt that the performance of speech recognition is significantly degraded by. Odyssey 2018 the speaker and language recognition workshop. For class p and speaker r cp,r is the centroid for each speaker speaker dependent v is speaker independent for new speaker model, from m, the vector ms is obtained by means of.
The sources are modeled using factoranalyzed hidden markov models hmm where source specific characteristics are captured by an eigenvoice speaker subspace model. Speaker recognition or broadly speech recognition has been an active area of research for the past two decades. An ivector extractor suitable for speaker recognition with both microphone and telephone speech mohammed senoussaoui 1. This paper presents a novel modeling approach named multieigenspace modeling technique based on regression class rcmes, which integrates the common eigenspace technique and the regression class rc idea of maximum likelihood linear regression mllr. The new speaker s model is a linear combination of the reference models. A double joint bayesian approach for jvector based textdependent speaker verification ziqiang shi, mengjiao wang, liu liu, huibin lin, rujie liu. Speech separation using speakeradapted eigenvoice speech models. About a third of the text is devoted to the background information needed for understanding speaker recognition technology. An ivector extractor suitable for speaker recognition with.
We do this by representing the space of speaker variation with a parametric signal modelbased on the eigenvoice technique for rapid speaker adaptation. For example, in all but the most recent nist speaker recognition evaluations sres, test utterance durations in the core condition range from 15 to 45. In this paper, we propose to combine eigenvoice and vts. Rapid speaker adaptation in eigenvoice space speech and. On the use of speaker superfactors for speaker recognition. Speech processing and the basic components of automatic speaker recognition systems are shown and design tradeoffs are discussed. Streambased speaker segmentation using speaker factors and eigenvoices. We propose a new jfa scoring method that is both symmetrical and efficient. Bnfbw for speaker and language id bottleneck features to speaker and language recognition 12 dnn for posteriors and bottleneck features, 21 frames of plp input 7 layers, 1024 hidden neurons, bottleneck layer 64 nodes, trained on swb 100hr data language recognition nist 2011 lre i. We apply the approach to speaker adaptation and speaker recognition. On deep speaker embeddings for textindependent speaker recognition. We tested factor analysis models having various numbers of speaker factors on the core condition and the extended data condition of the 2006 nist speaker recognition evaluation.
Keylemon was founded to provide highly accurate face recognition, speaker identification, and motion tracking solutions to developers and manufacturers across nuance looking at possible sale of the company. Speaker recognition a presentation by shamalee deshpande introduction speaker recognition automatically recognizing speaker uses individual information from the speakers speech waves introduction two approaches textdependant recognition textindependent recognition introduction two approaches textdependant recognition use of keywords or sentences having the same text for the. In the same way as means of gaussians can be concatenated to form a supervector, we use several estimates of speaker factors from the eigenvoice space to build a supervector of factors that we call superfactors. In this paper, the multistream based eigenvoice method is proposed in order to overcome the weak points of conventional eigenvoice and dimensional eigenvoice methods in fast speaker adaptation. We motivate the use of such factors in the current jfa model through. Automatic speaker recognition systems have a foundation built on ideas and techniques from the areas of speech science for speaker characterization, pattern recognition and engineering. Eigenvoice reestimation technique of acoustic models for speech recognition, speaker identification and speaker verification perronnin, florent. Speaker diarization based on bayesian hmm with eigenvoice. We will refer to these free parameters as speaker factors.
Eigenvoice speaker adaptation via composite kernel principal. Speaker recognition verification and identification. A speaker recognition system includes two primary components. The api can be used to determine the identity of an unknown speaker. Index termsspeaker recognition, eigenvoice, joint factor anal ysis, ivectors, evectors. Citeseerx document details isaac councill, lee giles, pradeep teregowda. In eigenvoice, the speaker acoustic space is described by a rectangular matrix. Bayesian speech and language processing by shinji watanabe. We also show how the performance of a speaker recognition system in the core test of the 2006 nist sre. Language recognition via ivectors and dimensionality.
Note that realtime speaker recognition is extremely hard, because we only use corpus of about 1 second length to identify the speaker. Enrollment for speaker identification is textindependent, which means that there are no restrictions on what the speaker says in the audio. An overview of textindependent speaker recognition. Speaker modeling technique based on regression class for. Introduction measurement of speaker characteristics. The role of age in factor analysis for speaker identification. In general, speaker recognition is used for discriminating people based on their voices. Firstly, very little data may be available for channel adaptation. Pdf rapid speaker adaptation in eigenvoice space robust.
Beware the difference between speaker recognition recognizing who is speaking and speech recognition recognizing what is being said. The role of speaker factors in the nist extended data task. In the side of adapting in speaker recognition system modeling, we will ameliorate conventional map maximum a posterior probability means to get speaker recognition model, apply mllr maximum likelihood linear regression and eigenvoice adaptation ways which used in speech recognition into adapting in speaker recognition system modeling, and. Ieee signal processing letters 1 kernel eigenvoices.
Nonetheless, due to many ker nel evaluations, both adaptation and subsequent recognition. Introduction it is no doubt that the performance of speech recognition is significantly degraded by mismatches between training. Combining eigenvoice speaker modeling and vtsbased. Is it possible to generate these or are they provided with the library. Communication systems and networks school of electrical and computer engineering. Improved ivectorbased speaker recognition for utterances. Sep 22, 2004 the second part is the ddhmm speaker recognition performed on the survived speakers after pruning. Relatedly, the eigenvoice technique has also been used to cluster speakers for improving speech recognition performance 6. The approach was inspired by the eigenfaces techniques used in face recognition. Pdf rapid speaker adaptation in eigenvoice space robust speech. Speaker adaptation in eigenvoice space is a popular method for rapid speaker adaptation. Prosodic features based textdependent speaker recognition.
Each speaker factor vector is projected back to the supervector model space by the eigenvoice matrix e using 1, to rapidly synthesize. Speaker identification determines which registered speaker provides a given utterance from amongst a set of known speakers. Przybocki national institute of standards and technology gaithersburg, md 20899 usa alvin. Specifically, we introduce eigenvoice speaker modeling for the clean speech into vtss nonlinear mismatch function. Use advanced ai algorithms for speaker verification and speaker identification. Joint factor analysis versus eigenchannels in speaker recognition patrick kenny, g. Rapid speaker adaptation in eigenvoice space robust speech recognition article pdf available in ieee transactions on speech and audio processing 86. Joint factor analysis versus eigenchannels in speaker. Eigenvoice used in speaker recognition with a few training. Each eigenvoice models a direction of interspeaker variability.
Inspired by the kernel eigenface idea in face recognition, kernel eigenvoice kev is proposed. Input audio of the unknown speaker is paired against a group of selected speakers and in the case there is a match found, the speakers identity is returned. Block diagram of a typical speaker recognition system. Sep 06, 2012 basic structures of speaker recognition systems all speaker recognition systems have to serve two distinguished phases. In the original rsw, the reference speakers are computed through a hierarchical speaker clustering hsc algorithm using information such as.
The main idea of this work is to exploit prior knowledge about the speaker space to find a low dimensional vector of speaker factors that summarize the salient speaker characteristics. Principal component analysis pca is a popular linear subspace learning technique and the approach that represents an arbitrary utterance or speaker as a linear combination of a set of basis voices based on pca is known as the eigenvoice approach. Pdf this paper describes a new modelbased speaker adaptation algorithm. In this work we built a lstm based speaker recognition system on a dataset collected from cousera lectures. Speaker identification apis allow you to identify who is speaking based on their voice, supporting scenarios such as conversation transcription. This incorporates kernel principal component analysis, a nonlinear version of principal component analysis, to capture higher order correlations in order to further explore the speaker space and enhance. We build a linear vector space of low dimensionality, called eigenspace, in which speakers are located.
Rapid speaker adaptation in eigenvoice space roland kuhn, jeanclaude junqua, member, ieee, patrick nguyen, and nancy niedzielski abstract this paper describes a new modelbased speaker adaptation algorithm called the eigenvoice approach. Index termsspeaker recognition, eigenvoice, joint factor anal ysis, ivectors, e vectors. The process of determining, if a suspected speaker is the source of trace, is called forensic speaker recognition. It can be divided into speaker identification and speaker verification. This paper examines and addresses a number of limitations and issues with the current schemes. Fundamentals of speaker recognition homayoon beigi. As the problem of identity theft and fraud is acute for the last decade speechpros speaker recognition technology can be applied to fight against it. Speech separation using speakeradapted eigenvoice speech.
Prosodic features based textdependent speaker recognition with short utterance. Our gui has basic functionality for recording, enrollment, training and testing, plus a visualization of realtime speaker recognition. An emerging technology, speaker recognition is becoming wellknown for providing voice authentication over the telephone for helpdesks. Using eigenvoice coefficients as features in speaker.
Pdf combining eigenvoice speaker modeling and vtsbased. The segmental eigenvoice method in 2 has been providing rapid speaker. Speaker subspace modeling has become increasingly important in speaker recognition, diarization, and clustering. In eigenvoice training for speaker recognition, all the recordings of a given speaker are considered to belong to the same person. Eigenvoice speaker adaptation has been shown to be effective in recent years.
The first oneis referred to the enrolment or training phase, while the second one is referred to as theoperational or testing phase. In the mean while, for the purpose of fixing the idea about srs, speech recognition will be introduced, and the distinctions between speech recognition. The basis vectors of this space are called eigenvoices. The role of age in factor analysis for speaker identi. Voice controlled devices also rely heavily on speaker recognition. Rapid speaker adaptation in eigenvoice space robust speech recognition. Speaker recognition antispeaker models identity claim bobsmodel figure 2. Due to its origin in textindependent speaker recognition, this paradigm does not make use of the phonetic content of each utterance. All content on this website, including dictionary, thesaurus, literature, geography, and other reference data is for informational purposes only.
Voice recognition or speaker recognition refers to the automated method of identifying or confirming the identity of an individual based on his voice. Eigenvoice reestimation technique of acoustic models for. This paper presents a streambased approach for unsupervised multi speaker conversational speech segmentation. Speaker recognition can be classified into identification and verification. Embedded kernel eigenvoice speaker adaptation and its.
In order to ensure strict disjointness between training and test sets, the factor analysis models were trained without using any of the data. It is an important topic in speech signal processing and has a variety of applications, especially in security systems. A multispectral data fusion approach to speaker recognition. Moreover, the uncertainty in the ivector estimates should be taken into account in the plda model, due to the short duration of the utterances. Combining eigenvoice speaker modeling and vtsbased environment compensation for robust speech recognition conference paper pdf available in acoustics, speech, and signal processing, 1988. Improving reference speaker weighting adaptation by the. In this chapter we provide an overview of the features, models, and classifiers derived from these areas that are the basis for modern automatic speaker. In this article, we present a new approach to modeling speaker dependent systems. It is the most exhaustive text on speaker recognition available. The speaker s voice is recorded, and a number of features are extracted to form a unique voiceprint. Classification methods for speaker recognition springerlink. Speaker verification also called speaker authentication contrasts with identification, and speaker recognition differs from speaker diarisation recognizing when the same speaker is speaking. By adding the speaker pruning part, the system recognition accuracy was increased 9.
Speaker recognition introduction measurement of speaker characteristics construction of speaker models decision and performance applications this lecture is based on rosenberg et al. Largevocabulary speech recognition zoi roupakia and mark gales, fellow, ieee abstractkernelised eigenvoice methods, which apply a nonlinear transform in speaker space, have previously been proposed for rapid adaptation. Presently, lawyers, law enforcement agencies, and judges in courts use speech and other biometric features to recognize suspects. But system description for dihard speech diarization. In the mean while, for the purpose of fixing the idea about srs, speech recognition will be introduced, and the distinctions between speech recognition and sr will be given too. Automatic speaker recognition for mobile forensic applications. Our model is a bayesian hidden markov model, in which states represent speaker specific distributions and transitions between states represent speaker turns. Bayesian analysis of speaker diarization with eigenvoice. Modelling, feature extraction and effects of clinical environment a thesis submitted in fulfillment of the requirements for the degree of doctor of philosophy sheeraz memon b. Keywords eigenvoice speaker adaptation, kernel eigen voice speaker.
To improve the performance of the method and to obtain stabilized results, the number of speaker dependent. Refer to comparison of scoring methods used in speaker recognition with joint factor analysis by glembek, et. In the field of automatic speech recognition, eigenvoice speaker adaptation 3 has. Speech separation using speaker adapted eigenvoice speech models. 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. Matejka, speaker diarization based on bayesian hmm with eigenvoice priors, in proceedings of odyssey 2018, the speaker and language recognition workshop, 2018. As in the ivector or jfa models, speaker distributions are modeled by gmms with parameters constrained by eigenvoice priors. Speaker recognition is the process of automatically recognizing who is speaking on the basis of individual information included in speech signals. The speaker recognition process based on a speech signal is treated as one of the most exciting technologies of human recognition orsag 2010.
Automatic speaker recognition is the use of a machine to recognize a person from a spoken phrase. Spoken passphrase verification in the ivector space hossein zeinali, lukas burget, hossein sameti, honza cernocky. A compact representation of speakers in model space. The proposed algorithm is able to learn adaptation parameters for two speech sources when only a mixture of signals is observed. Unlike other approaches to the problem of estimating.
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