Eigenvoice reestimation technique of acoustic models for speech recognition, speaker identification and speaker verification perronnin, florent. 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. Speaker recognition systems are continuously evolving and a lot of research is being conducted in this domain with constant variations and experimentation to be able to. 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. Dimensionality reduction techniques are al ready widely used in speech recognition. All eigenvoice dimensions were retained for these models, e. The role of age in factor analysis for speaker identi. The term voice recognition can refer to speaker recognition or speech recognition. The design of recent 2010 nistsrespeaker recognition evaluation sre re. Specifically, we introduce eigenvoice speaker modeling for the clean speech into vtss nonlinear mismatch function. In summary, we have described a novel monaural source separation system.
As in the ivector or jfa models, speaker distributions are modeled by gmms with parameters constrained by eigenvoice priors. By considering the case where enrollment and test phrases. Combining eigenvoice speaker modeling and vtsbased. 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.
In 1, the principal component analysis pca employed to find the most. This ivectorpldaahc based system will also serve as the baseline for our experiments. 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. Eigenvoice based methods have been shown to be effective for fast speaker adaptation when only a small amount of adaptation data is available. An emerging technology, speaker recognition is becoming wellknown for providing voice authentication over the telephone for helpdesks. In jeong and kim 2010, the hmm mean vectors of each training speaker were arranged in a matrix and twodimensional pca 2dpca yang et al. Joint factor analysis versus eigenchannels in speaker. 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. The timedomain sources were reconstructed from the stft magnitude estimates x. Dumouchel abstractwe compare two approaches to the problem of session variability in gmmbased speaker veri. Citeseerx onetomany voice conversion based on tensor.
Part of the lecture notes in computer science book series lncs, volume 4343. Whether one is a faculty, an engineer, a researcher or a student, heshe will find in fundamentals of speaker. Speaker recognition using evectors acm digital library. 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. This paper presents a streambased approach for unsupervised multi speaker conversational speech segmentation. The approach was inspired by the eigenfaces techniques used in face recognition. We propose a new jfa scoring method that is both symmetrical and efficient. Speech separation using speakeradapted eigenvoice speech.
Speaker recognition known as voiceprint recognition in industry is the process of. 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. Speaker modeling technique with sparse training data is an active branch of robust speaker recognition research. A training tensor composed of speaker dependent models is decomposed by parallel factor analysis, which can. Automatic speaker recognition is the use of a machine to recognize a person from a spoken phrase. 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.
This toolbox contains a collection of matlab tools and routines that can be used for research and development in speaker recognition. In this article, we present a new approach to modeling speaker dependent systems. Unlike other approaches to the problem of estimating. Speaker diarization based on bayesian hmm with eigenvoice. In voice conversion studies, realization of conversion fromto an arbitrary speaker s voice is one of the important objectives. Each speaker factor vector is projected back to the supervector model space by the eigenvoice matrix e using 1, to rapidly synthesize. Inspired by the kernel eigenface idea in face recognition, kernel eigenvoice kev is proposed. In this paper, we propose to combine eigenvoice and vts. Pdf rapid speaker adaptation in eigenvoice space robust. Speaker verification also called speaker authentication contrasts with identification, and speaker recognition differs from speaker diarisation recognizing when the same. Przybocki national institute of standards and technology gaithersburg, md 20899 usa alvin. Citeseerx new map estimates for speaker recognition. Voice controlled devices also rely heavily on speaker recognition. Ourwork, whichismainlyinspiredby 18, applies the same eigenvoice.
Automatic speech recognition phoneme recognition speaker adaptation cmu. Use advanced ai algorithms for speaker verification and speaker identification. In eigenvoice training for speaker recognition, all the recordings of a given speaker are considered to belong to the same person. Using eigenvoice coefficients as features in speaker. We report the results of some experiments which demonstrate that eigenvoice map and eigenphone map are at least as effective as classical map for discriminative speaker modeling on switchboard data. The approach constrains the adapted model to be a linear combination. Despite the success of the ivectorplda paradigm, its applicability in textdependent speaker recognition remains questionable. The approach constrains the adapted model to be a linear combination of a small number of basis.
Communication systems and networks school of electrical and computer engineering. The result is 942 pages of a good academically structured literature. The eigenvoice technique that was introduced for rapid speaker adaptation is a speaker clusteringbased adaptation approach kuhn et al. Our gui has basic functionality for recording, enrollment, training and testing, plus a visualization of realtime speaker recognition. 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.
Speech processing and the basic components of automatic speaker recognition systems are shown and design tradeoffs are discussed. Firstly, very little data may be available for channel adaptation. 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. The new speaker s model is a linear combination of the reference models. Streambased speaker segmentation using speaker factors and eigenvoices. Speaker recognition antispeaker models identity claim bobsmodel figure 2. Citeseerx document details isaac councill, lee giles, pradeep teregowda. In eigenvoice, the speaker acoustic space is described by a rectangular matrix. 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. 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.
Text dependent speaker verification and text independent speaker identification manjula subramanian, sachit mohan, anuradha mahajan on. 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. Introduction it is no doubt that the performance of speech recognition is significantly degraded by mismatches between training. Speaker recognition can be classified into text dependent and the text independent methods. It is an important topic in speech signal processing and has a variety of applications, especially in security systems. An overview of textindependent speaker recognition. Ourwork, whichismainlyinspiredby 18, applies the same eigenvoice priors and similar vb infer.
Speaker recognition introduction measurement of speaker characteristics construction of speaker models decision and performance applications this lecture is based on rosenberg et al. Enrollment for speaker identification is textindependent, which means that there are no restrictions on what the speaker says in the audio. Unsupervised rapid speaker adaptation based on selective. This paper describes a new modelbased speaker adaptation algorithm called the eigenvoice approach. The basis vectors of this space are called eigenvoices. But system description for dihard speech diarization. Odyssey 2018 the speaker and language recognition workshop. Speaker recognition is the identification of a person from characteristics of voices. The book entitled introduction to speaker recognition,applications and techniques tries to deal with the fundamental issues of basic speaker recognition techniques related with speech science and technology. Speaker diarization based on bayesian hmm with eigenvoice priors mireia diez, lukas burget, pavel matejka. Pdf rapid speaker adaptation in eigenvoice space robust speech.
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. It can be used for authentication, surveillance, forensic speaker recognition and a number of related activities. To evaluate this hypothesis we separated the ssc training data into random subsets of 10, 20, and 30 speakers and trained new eigenvoice models from each subset. Telephone and auxiliarymicrophone recorded sp eech emphasize the need for a robust way of dealing with unwanted variation. Good performance was obtained by the eigenvoice model with k 20 and by the tensorvoice model with k mix 70, k dim 35, k speaker 30, and k noise 2. This repository contains python programs that can be used for automatic speaker recognition. The traditional way to achieve such rapid adaptation is the eigenvoice technique which works well in speech recognition but known to generate perceptual artifacts in statistical speech synthesis.
Eigenvoice speaker adaptation with minimal data for. Burget, analysis of variational bayes eigenvoice hidden markov model based speaker diarization, to be published, 2019. Speaker verification apis serve as an intelligent tool to help verify speakers using both their voice and speech passphrases. A multispectral data fusion approach to speaker recognition. Voice recognition or speaker recognition refers to the automated method of identifying or confirming the identity of an individual based on his voice. Eigenvoice used in speaker recognition with a few training. We are happy to announce the release of the msr identity toolbox. An ivector extractor suitable for speaker recognition with both microphone and telephone speech mohammed senoussaoui 1. In order to ensure strict disjointness between training and test sets, the factor analysis models were trained without using any of the data.
This book provides an overview of a wide range of fundamental theories of bayesian learning, inference, and prediction for uncertainty modeling in speech and language processing. Speaker verification also called speaker authentication contrasts with identification, and speaker recognition differs from speaker diarisation recognizing when the same speaker is speaking. This book discusses large margin and kernel methods for speech and speaker recognition. Matejka, speaker diarization based on bayesian hmm with eigenvoice priors, in proceedings of odyssey 2018, the speaker and language recognition workshop, 2018. Experimental results for a smallvocabulary task letter recognition given in the paper show that the approach yields major improvements in performance for tiny amounts of adaptation data. This book is developed based on the research works carried out in speech signal processing specially in the area of speaker. Eigenvoice reestimation technique of acoustic models for. Automatic speaker recognition systems have a foundation built on ideas and. We also show how the performance of a speaker recognition system in the core test of the 2006 nist sre. Using eigenvoice coefficients as features in speaker recognition. Introduction measurement of speaker characteristics. Joint speaker and environment adaptation using tensorvoice. For this purpose, eigenvoice conversion evc based on an.
Speaker identification apis allow you to identify who is speaking based on their voice, supporting scenarios such as conversation transcription. 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. Fundamentals of speaker recognition homayoon beigi on. 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 compare the results with map means. The speaker s voice is recorded, and a number of features are extracted to form a unique voiceprint. An emerging technology, speaker recognition is becoming wellknown for providing voice authentication over the telephone for helpdesks, call centres and other enterprise businesses for business. Rapid speaker adaptation in eigenvoice space speech and audio. Beware the difference between speaker recognition recognizing who is speaking and speech recognition recognizing what is being said. Fundamentals of speaker recognition homayoon beigi. Chandra 2 department of computer science, bharathiar university, coimbatore, india suji.
In this thesis, we concentrate ourselves on speaker recognition systems srs. Pandey abstract this paper aims at providing a brief overview into the area of speaker recognition. Part of the lecture notes in computer science book series lncs, volume 8509. In this work we built a lstm based speaker recognition system on a dataset collected from cousera lectures. Table 3 shows the word recognition accuracy of the eigenvoice method, the tensorvoice method, and the mllr method, and fig. As summarized in jolliffes book on pca, the standard reference. Eigenvoice and vector taylor series vts are good models for speaker differences and environmental variations separately. Language recognition via ivectors and dimensionality. Speaker recognition can be classified into identification and verification. Comparison of speaker recognition approaches for real. Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems. Joint factor analysis versus eigenchannels in speaker recognition patrick kenny, g. 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.
The two major discrepancies between the training and deployment conditions in automatic speech recognition are speaker and noise environment. Speaker recognition or broadly speech recognition has been an active area of research for the past two decades. The api can be used to determine the identity of an unknown speaker. Unsupervised rapid speaker adaptation based on selective eigenvoice merging 3. Speaker recognition using deep belief networks cs 229 fall 2012. However, the eigenvoice approach based on pca was developed only for the adaptation of hmm mean vectors 5.
Comparison of speaker recognition approaches for real applications. This paper describes a novel approach to flexible control of speaker characteristics using tensor representation of speaker space. The separation algorithm described above was run for fifteen iterations using eigenvoice speech models trained on all 34 speakers in the data set. Rapid speaker adaptation in eigenvoice space speech and. Refer to comparison of scoring methods used in speaker recognition with joint factor analysis by glembek, et. Select the testing console in the region where you created your resource. Eigenvoice speaker adaptation has been shown to be effective in recent years. Index termsspeaker recognition, eigenvoice, joint factor anal ysis, ivectors. Presented is a speaker adaptation method which is robust to noise environments in the framework of the basisbased technique.
Researchinthe speaker recognition community has continued toaddress methods of mitigating variati onal nuisances. 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. We show how eigenvoice map can be modified to yield a new modelbased channel compensation technique which. Variational manifold learning for speaker recognition. By writing fundamentals of speaker recognition, homayoon beigi took up the challenge to compose a comprehensive book on a rapidly growing scientific field. It makes use of the prior knowledge of training speakers to provide a fast adaptation algorithm in other words, only a small amount of adaptation data is needed. Inspired by the kernel eigenface idea in face recognition, kernel eigenvoice kev is. It is the most exhaustive text on speaker recognition available. The role of age in factor analysis for speaker identification. On the use of speaker superfactors for speaker recognition. Experimental results for a smallvocabulary task letter recognition given in the paper show that the approach yields major improvements in performance for tiny amounts. Speech separation and recognition challenge sciencedirect.
In the last decade, eigenvoice ev speaker adaptation has been developed. Its principle is to construct a new speaker model as a linear combination of a. In a similar approach using basis vectors, the speaker adaptation using weight matrix in jeong and kim 2010 showed better performance than eigenvoice adaptation as the amount of adaptation data increased e. The speaker recognition process based on a speech signal is treated as one of the most exciting technologies of human recognition orsag 2010. An ivector extractor suitable for speaker recognition with.
Classification methods for speaker recognition springerlink. The rst vb approach to sd was proposed in 16,17 and furtherextendedin18. In this paper, we propose to use eigenvoice coefficients as features for speaker recognition. Bayesian speech and language processing by shinji watanabe. Speaker modeling technique based on regression class for. About a third of the text is devoted to the background information needed for understanding speaker recognition technology.
We build a linear vector space of low dimensionality, called eigenspace, in which speakers are located. Each eigenvoice models a direction of inter speaker variability. Large margin and kernel methods is a collation of research in the recent advances in large margin and kernel methods, as applied to the field of speech and speaker recognition. Asr is done by extracting mfccs and lpcs from each speaker and then forming a speaker specific codebook of the same by using vector quantization i like to think of it as a fancy. Speaker adaptation is an important technology to finetune either features or speech models for. Speech separation using speakeradapted eigenvoice speech models. Note that realtime speaker recognition is extremely hard, because we only use corpus of about 1 second length to identify the speaker. The role of speaker factors in the nist extended data task. It provides researchers with a test bed for developing new frontend and backend techniques. Rapid speaker adaptation in eigenvoice space robust speech recognition. However, speaker and environmental variation always coexist in realworld speech. Speaker recognition in a multi speaker environment alvin f martin, mark a.
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