An overview of textindependent speaker recognition. Speaker recognition using deep belief networks cs 229 fall 2012. We build a linear vector space of low dimensionality, called eigenspace, in which speakers are located. 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 ivectorpldaahc based system will also serve as the baseline for our experiments. 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. Speaker adaptation is an important technology to finetune either features or speech models for. 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. Our model is a bayesian hidden markov model, in which states represent speaker specific distributions and transitions between states represent speaker turns. Speech separation using speakeradapted eigenvoice speech models. Introduction measurement of speaker characteristics. 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. 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.
Speaker verification also called speaker authentication contrasts with identification, and speaker recognition differs from speaker diarisation recognizing when the same speaker is speaking. 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. Bayesian analysis of speaker diarization with eigenvoice. Telephone and auxiliarymicrophone recorded sp eech emphasize the need for a robust way of dealing with unwanted variation. Voice recognition or speaker recognition refers to the automated method of identifying or confirming the identity of an individual based on his voice. Its principle is to construct a new speaker model as a linear combination of a. A multispectral data fusion approach to speaker recognition. Speaker modeling technique based on regression class for. The term voice recognition can refer to speaker recognition or speech recognition. Speaker recognition known as voiceprint recognition in industry is the process of. Speaker recognition in a multi speaker environment alvin f martin, mark a.
But system description for dihard speech diarization. Table 3 shows the word recognition accuracy of the eigenvoice method, the tensorvoice method, and the mllr method, and fig. Speech separation using speakeradapted eigenvoice speech. This paper describes a new modelbased speaker adaptation algorithm called the eigenvoice approach. Speaker diarization based on bayesian hmm with eigenvoice. 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.
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. 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. 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. Speaker recognition can be classified into text dependent and the text independent methods. Speaker modeling technique with sparse training data is an active branch of robust speaker recognition research. Eigenvoice reestimation technique of acoustic models for speech recognition, speaker identification and speaker verification perronnin, florent. Experimental results for a smallvocabulary task letter recognition given in the paper show that the approach yields major improvements in performance for tiny amounts. Automatic speaker recognition algorithms in python. Presented is a speaker adaptation method which is robust to noise environments in the framework of the basisbased technique. In this article, we present a new approach to modeling speaker dependent systems. 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 paper, we propose to combine eigenvoice and vts. Refer to comparison of scoring methods used in speaker recognition with joint factor analysis by glembek, et.
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. The speaker s voice is recorded, and a number of features are extracted to form a unique voiceprint. 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. Using eigenvoice coefficients as features in speaker. Eigenvoice based methods have been shown to be effective for fast speaker adaptation when only a small amount of adaptation data is available. Eigenvoice speaker adaptation has been shown to be effective in recent years. The new speaker s model is a linear combination of the reference models. The rst vb approach to sd was proposed in 16,17 and furtherextendedin18. However, speaker and environmental variation always coexist in realworld speech.
Burget, analysis of variational bayes eigenvoice hidden markov model based speaker diarization, to be published, 2019. We show how eigenvoice map can be modified to yield a new modelbased channel compensation technique which. The role of age in factor analysis for speaker identification. The basis vectors of this space are called eigenvoices. 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. Eigenvoice and vector taylor series vts are good models for speaker differences and environmental variations separately. It is the most exhaustive text on speaker recognition available. Each eigenvoice models a direction of inter speaker variability. Fundamentals of speaker recognition homayoon beigi.
Eigenvoice speaker adaptation with minimal data for. 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. Citeseerx onetomany voice conversion based on tensor. The api can be used to determine the identity of an unknown speaker. In voice conversion studies, realization of conversion fromto an arbitrary speaker s voice is one of the important objectives. For this purpose, eigenvoice conversion evc based on an. 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. The speaker recognition process based on a speech signal is treated as one of the most exciting technologies of human recognition orsag 2010.
It can be used for authentication, surveillance, forensic speaker recognition and a number of related activities. It is an important topic in speech signal processing and has a variety of applications, especially in security systems. On the use of speaker superfactors for 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. This paper presents a streambased approach for unsupervised multi speaker conversational speech segmentation. Eigenvoice reestimation technique of acoustic models for.
The approach constrains the adapted model to be a linear combination. Our gui has basic functionality for recording, enrollment, training and testing, plus a visualization of realtime speaker recognition. We also show how the performance of a speaker recognition system in the core test of the 2006 nist sre. The separation algorithm described above was run for fifteen iterations using eigenvoice speech models trained on all 34 speakers in the data set.
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. 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 order to ensure strict disjointness between training and test sets, the factor analysis models were trained without using any of the data. Index termsspeaker recognition, eigenvoice, joint factor anal ysis, ivectors. Dimensionality reduction techniques are al ready widely used in speech recognition. We are happy to announce the release of the msr identity toolbox. 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.
Speaker recognition introduction measurement of speaker characteristics construction of speaker models decision and performance applications this lecture is based on rosenberg et al. Streambased speaker segmentation using speaker factors and eigenvoices. 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. Part of the lecture notes in computer science book series lncs, volume 8509. Beware the difference between speaker recognition recognizing who is speaking and speech recognition recognizing what is being said. Odyssey 2018 the speaker and language recognition workshop. A training tensor composed of speaker dependent models is decomposed by parallel factor analysis, which can. All 33 eigenvoice dimensions were used for adaptation. Pdf rapid speaker adaptation in eigenvoice space robust.
By considering the case where enrollment and test phrases. Pandey abstract this paper aims at providing a brief overview into the area of speaker recognition. Voice controlled devices also rely heavily on speaker recognition. Ourwork, whichismainlyinspiredby 18, applies the same eigenvoice priors and similar vb infer. It provides researchers with a test bed for developing new frontend and backend techniques. Speech processing and the basic components of automatic speaker recognition systems are shown and design tradeoffs are discussed. Speaker recognition can be classified into identification and verification. The design of recent 2010 nistsrespeaker recognition evaluation sre re. Researchinthe speaker recognition community has continued toaddress methods of mitigating variati onal nuisances. Speaker recognition using evectors acm digital library.
In 1, the principal component analysis pca employed to find the most. In this thesis, we concentrate ourselves on speaker recognition systems srs. Inspired by the kernel eigenface idea in face recognition, kernel eigenvoice kev is proposed. As in the ivector or jfa models, speaker distributions are modeled by gmms with parameters constrained by eigenvoice priors. Przybocki national institute of standards and technology gaithersburg, md 20899 usa alvin. Text dependent speaker verification and text independent speaker identification manjula subramanian, sachit mohan, anuradha mahajan on. In summary, we have described a novel monaural source separation system. The timedomain sources were reconstructed from the stft magnitude estimates x. However, the eigenvoice approach based on pca was developed only for the adaptation of hmm mean vectors 5. The approach constrains the adapted model to be a linear combination of a small number of basis. Automatic speech recognition phoneme recognition speaker adaptation cmu. This book is developed based on the research works carried out in speech signal processing specially in the area of speaker.
Whether one is a faculty, an engineer, a researcher or a student, heshe will find in fundamentals of speaker. The two major discrepancies between the training and deployment conditions in automatic speech recognition are speaker and noise environment. 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. Part of the lecture notes in computer science book series lncs, volume 4343.
Firstly, very little data may be available for channel adaptation. Speaker recognition or broadly speech recognition has been an active area of research for the past two decades. Citeseerx document details isaac councill, lee giles, pradeep teregowda. This toolbox contains a collection of matlab tools and routines that can be used for research and development in speaker recognition. In eigenvoice, the speaker acoustic space is described by a rectangular matrix.
All eigenvoice dimensions were retained for these models, e. This paper describes a novel approach to flexible control of speaker characteristics using tensor representation of speaker space. Joint factor analysis versus eigenchannels in speaker. Communication systems and networks school of electrical and computer engineering. The eigenvoice technique that was introduced for rapid speaker adaptation is a speaker clusteringbased adaptation approach kuhn et al. Speaker identification apis allow you to identify who is speaking based on their voice, supporting scenarios such as conversation transcription. In eigenvoice training for speaker recognition, all the recordings of a given speaker are considered to belong to the same person. Note that realtime speaker recognition is extremely hard, because we only use corpus of about 1 second length to identify the speaker.
The result is 942 pages of a good academically structured literature. Dumouchel abstractwe compare two approaches to the problem of session variability in gmmbased speaker veri. The role of age in factor analysis for speaker identi. 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. Introduction it is no doubt that the performance of speech recognition is significantly degraded by mismatches between training.
Automatic speaker recognition is the use of a machine to recognize a person from a spoken phrase. Select the testing console in the region where you created your resource. Use advanced ai algorithms for speaker verification and speaker identification. The approach was inspired by the eigenfaces techniques used in face recognition. Unsupervised rapid speaker adaptation based on selective. Ourwork, whichismainlyinspiredby 18, applies the same eigenvoice. Classification methods for speaker recognition springerlink. Pdf rapid speaker adaptation in eigenvoice space robust speech. Using eigenvoice coefficients as features in speaker recognition. Automatic speaker recognition systems have a foundation built on ideas and. This repository contains python programs that can be used for automatic speaker recognition. 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. Specifically, we introduce eigenvoice speaker modeling for the clean speech into vtss nonlinear mismatch function. Inspired by the kernel eigenface idea in face recognition, kernel eigenvoice kev is.
Each speaker factor vector is projected back to the supervector model space by the eigenvoice matrix e using 1, to rapidly synthesize. About a third of the text is devoted to the background information needed for understanding speaker recognition technology. Enrollment for speaker identification is textindependent, which means that there are no restrictions on what the speaker says in the audio. Speaker recognition antispeaker models identity claim bobsmodel figure 2.
Comparison of speaker recognition approaches for real applications. 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. We propose a new jfa scoring method that is both symmetrical and efficient. 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. Joint speaker and environment adaptation using tensorvoice. 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. Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems. Speaker recognition is the identification of a person from characteristics of voices. Combining eigenvoice speaker modeling and vtsbased. Language recognition via ivectors and dimensionality. 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. Chandra 2 department of computer science, bharathiar university, coimbatore, india suji.
Eigenvoice used in speaker recognition with a few training. Comparison of speaker recognition approaches for real. Despite the success of the ivectorplda paradigm, its applicability in textdependent speaker recognition remains questionable. As summarized in jolliffes book on pca, the standard reference. An emerging technology, speaker recognition is becoming wellknown for providing voice authentication over the telephone for helpdesks. Bayesian speech and language processing by shinji watanabe. The role of speaker factors in the nist extended data task. Speaker verification apis serve as an intelligent tool to help verify speakers using both their voice and speech passphrases. An ivector extractor suitable for speaker recognition with both microphone and telephone speech mohammed senoussaoui 1. In this work we built a lstm based speaker recognition system on a dataset collected from cousera lectures.
Speaker verification also called speaker authentication contrasts with identification, and speaker recognition differs from speaker diarisation recognizing when the same. Citeseerx new map estimates for 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. In the last decade, eigenvoice ev speaker adaptation has been developed.
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. 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. Speaker diarization based on bayesian hmm with eigenvoice priors mireia diez, lukas burget, pavel matejka. Rapid speaker adaptation in eigenvoice space robust speech recognition. Unsupervised rapid speaker adaptation based on selective eigenvoice merging 3. 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. By writing fundamentals of speaker recognition, homayoon beigi took up the challenge to compose a comprehensive book on a rapidly growing scientific field. Speech separation and recognition challenge sciencedirect. Fundamentals of speaker recognition homayoon beigi on. In this paper, we propose to use eigenvoice coefficients as features for speaker recognition. Unlike other approaches to the problem of estimating. This book discusses large margin and kernel methods for speech and speaker recognition.
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