Speaker verification based on fusion of acoustic and. Speaker recognition or broadly speech recognition has been an active area of research for the past two decades. If two people say the same sentence, the computer should deliver the same answer. This paper gives an overview of automatic speaker recognition technology, with an emphasis on textindependent recognition. In this paper, we test an adaptation of the t norm we define a matched comparison as comparing two technique used in the domain of speaker verification, for recordings in similar conditions e. Voice recognition is the art of identifying what a person is saying. The best result we obtained was a detection cost of 0. Forensic speaker recognition is a useful book for forensic speech scientists, speech signal processing experts, speech system developers, criminal prosecutors and counterterrorism intelligence officers and agents. In this paper we survey some of the main techniques used, and point out possible future directions and asyetunexplored opportunities. Introduction improved user security in speechdriven telephony applications can be achieved with automatic speaker veri. By writing fundamentals of speaker recognition, homayoon beigi took up the challenge to compose a comprehensive book on a rapidly growing scientific field.
Can we help the cnn discover more meaningful filters. Examples of the modeldomain compensation methods include the speaker independent variance transformation 17, and the transformation for synthesizing supplementary speaker models for other channel types from multichannel training data 18. Both z norm and t norm 14 score normalization approaches were applied separately for each gender. It is less frequently applied, however, to textdependent or textprompted speaker recognition, mainly because its improvement in this context is more modest. This book discusses large margin and kernel methods for speech and speaker recognition. Law enforcement and counterterrorism is an anthology of the research findings of 35 speaker recognition experts from around the world. Datadriven impostor selection for tnorm score normalisation. We found that factor analysis was far more effective than eigenchannel modeling. Index termsspeaker verification, score normalization, sta. Proceedings of the 1st international conference acii 05, beijing, china, october 2224, 2005, vol. In many speaker recognition evaluations, the utterances were typically con. The second challenge is dataset shift from switchboard swb and mixer datasets used in the previous sres to the new call my net.
Advancements and challenges 3 the elements in the upper triangle of s g including the diagonal elements. The speaker and language recognition workshop, stellenbosch, south africatest normalization t norm is a score normalization technique that is regularly and successfully applied in the context of textindependent speaker recognition. Leaveoneout method for t norm model using the t norm speaker as class 0 and the background speaker set as class 1. Recent works have proposed directly feeding cnns with raw waveforms. For generality, let us consider the full set of cohort scores. Normalization and transformation tech niques for robust speaker recognition 3 ben et al. Score fusion two methods were investigated for training the weights in. On the other hand, if s g is a diagonal matrix, then, u s gd d s g dd 8 d 2f 1,2,d g 3 therefore, we may always reconstruct s g from u g using the inverse transformation, s g u g 1 4 the parameter vector for the mixture model may be constructed as follows.
Bengio, speaker recognition from raw waveform with sincnet, in proc. It presents theoretical and practical foundations of these methods, from support vector machines to. T norm, zt norm and s norm we call this selection adaptive, as the selected cohort might change for every speaker. The most prevalent methods include h norm 14, z norm 15, and t norm 16. Speaker recognition is the capability of a software or hardware to receive speech signal, identify the speaker present in the speech signal and recognize the speaker afterwards. Score normalization is an important component in most speech classification tasks including speaker recognition.
Part of the lecture notes in computer science book series lncs, volume 5558. Wu, improving speaker recognition by training on emotionadded models, in affective computing and intelligent interaction. Score normalization technique for textprompted speaker. The t norm model is trained with a leaveoneout method, and the same speaker utterances are excluded to train its own t model.
Infotechdist submitted as a requirement of the degree of doctor of philosophy at the queensland university of technology. Score normalization for textindependent speaker verification. Speaker and speech recognition from raw waveform with sincnet. It is characterized in adults with the production of about 14 different sounds per second via the harmonized actions of roughly 100 muscles. Extraction methods for speaker recognition jianglin wang, b. Marquette university, 20 speaker recognition has received a great deal of attention from the speech community, and signi cant gains in robustness and accuracy have been obtained over the past decade. Efficient score normalization for speaker recognition. A generative model for score normalization in speaker recognition. Mar 19, 2010 efficient score normalization for speaker recognition abstract.
In this work we built a lstm based speaker recognition system on a dataset collected from cousera lectures. To deal with the difficulties, robust speaker recognition open access database is such a topic for study. Automatic speaker verification on site and by telephone diva. Speech recognition, technologies and applications, book edited by. The proposed technique is based on the widely used testnormalization method tnorm, which compensates testdependent variability using a fixed cohort of impostors. Jan 01, 2007 a novel score normalization scheme for speaker verification is presented. Application of cohort selection in speaker verification is still being continued. We start with the fundamentals of automatic speaker recognition, concerning. The nist 1999 speaker recognition evaluationan overview digital signal process. Index terms speaker verification, score normalization, sta. Over the last decade, speaker recognition technology has made its debut in. In the original s norm method 9, imposter sets for t norm and z norm parts are the same and so it is symmetric. An advantage of this approach is that we can construct an svm speaker recognition system and then just cycle the t norm speakers through the system as if they were.
Joint factor analysis versus eigenchannels in speaker. We highlight the improvements made to specific subsystems and analyze the performance of various subsystem combinations in different data conditions. Richard is a member of the elm core team, the author of elm in action from manning publications, and the instructor for the intro to elm and advanced elm cou. Training universal background models for speaker recognition. Dataset the primary training data is the combination of telephony parts from nist sre 2004 2008, fisher english and switchboard. Znorm implementations in this framework are then given in sections iva and ivb. The final normalization of interest is test normalization, or t norm, which. We proposed a novel endtoend approach to speaker verifica tion, which directly.
Test normalization tnorm is a score normalization technique that is regularly and successfully applied in the context of textindependent speaker recognition. Nist sre 2005 data was used for t norm to normalize the decision score obtained with the svm system 14. We start with the fundamentals of automatic speaker recognition, concerning feature extraction and speaker modeling. Ieee international conference on acoustics, speech and signal processing. We elaborate advanced computational techniques to address robustness and session. However, in this paper, it is proposed based on the following proposition before being val. Pdf speaker recognition using concatenated phoneme models.
Jul, 2020 this paper extends this technique to the refinement of the t norm dataset for svmbased speaker verification. Improvements in factor analysis based speaker verification. We randomly selected a small part of the english corpus speaker recognition tool since experiment can be easily run on a local machine, and the results can be obtained in a reasonnable amount of time norm, i. Improved ivector speaker verification based on wccn and.
Improved ivector speaker verification based on wccn and ztnorm. The independent refinement of the background and t norm datasets provides a means of investigating the sensitivity of svmbased speaker verification performance to the selection of each of these datasets. Phoneme and subphoneme tnormalization for text dependent. Costsensitive learning for emotion robust speaker recognition. We give an overview of both the classical and the stateoftheart methods.
An overview of textindependent speaker recognition. Reynolds, senior member, ieee abstractthis paper investigates the problem of speaker identi. It is less frequently applied, however, to textdependent or textprompted speaker recognition, mainly because its. Speaker verification is the biometric task of authenticating a claimed identity by means of analyzing. Box 218, yorktown heights, ny 10598 abstract the effect of utterance length on the estimation of the likelihood of a speaker has previously seen a brief treatment in past works. Finding difficult speakers in automatic speaker recognition by lara. Jan 01, 2000 the nist 1999 speaker recognition evaluationan overview digital signal process.
The 20 speaker recognition evaluation in mobile environment. Analysis of score normalization in multilingual speaker. Svm speaker modeling, the score is calculated from the target svm and a gmm trained from the trial utterance. We demonstrate the effectiveness of zt norm score normalization and a new decision criterion for speaker recognition which can handle large numbers of t norm speakers and large numbers of speaker. Normalization and transformation techniques for robust. We discuss an extension to the widely used score normalization technique of test normalization tnorm for textindependent speaker verification.
Recent years, however, have seen widespread use of speech recognition in advanced speaker recognition research systems, such as those fielded in the annual nist speaker recognition evaluation sre. A novel score normalization scheme for speaker verification is presented. Recently, deep learning has dramatically revolutionized speaker. Dynamic time warping can not be applied to textindependent speaker recognition, due to the lack of temporal alignment between the sequenceof feature vectors from training and testing utterances. For speaker verification, the score of each trial utterance against its hypothesized speaker is compared against a. Speaker adaptive cohort selection for tnorm in textindependent. Further details about the various systems are described in the experiments section. Normalization techniques include z norm, t norm, zt norm and tz norm. Pdf on compensation of mismatched recording conditions in.
The second challenge is dataset shift from switchboard swb and mixer datasets. Nist 2010 speaker recognition evaluation data for telephonetelephone condition 25. Z norm was trained on 904 sre 2004 speakers and t norm was trained on 229 speakers. Speaker recognition is a task of identifying persons from their voices. Pdf an overview of textindependent speaker recognition. Automatic speaker recognition under adverse conditions robert j. Tnorm based score normalization is performed using cohorts. The sri speaker recognition system for the 2008 nist speaker recognition evaluation sre incorporates a variety of models and features, both cepstral and stylistic. A novel normalization technique, test normalization, is introduced. Apr 23, 2007 we demonstrate the effectiveness of zt norm score normalization and a new decision criterion for speaker recognition which can handle large numbers of t norm speakers and large numbers of speaker factors at little computational cost. The result is 942 pages of a good academically structured literature.
Speaker verification using speaker and testdependent. Z norm implementations in this framework are then given in sections iva and ivb. The crss systems for the 2010 nist speaker recognition evaluation. Speaker verification using speaker and testdependent fast. However, hter is adopted in this evaluation in order to use the same terminology as for face recognition. The volume provides a multidimensional view of the complex science involved in determining whether a suspects voice truly matches forensic speech samples, collected by law enforcement and counterterrorism agencies, that.
Speaker and speech recognition from raw waveform with. The volume provides a multidimensional view of the complex science involved in determining whether a suspects voice truly matches forensic speech samples, collected by law. However, the features used for identi cation are still primarily rep. Automatic speaker recognition under adverse conditions. Oct 04, 2017 speech is a complex naturally acquired human motor ability. The important information is the message being delivered. At the score level, we found score normalization s norm, t norm and the use of quality measure as part of score calibration were useful. Stateoftheart scoring approaches use both t norm and z norm.
Speaker verification using speaker speech technology group. Speaker recognition has been studied actively for several decades. Most of the textindependent speaker recognition systems rely on the probabilistic modeling of the set of feature vectors. The task of automatic speaker recognition, wherein a system verifies or. Studies on model distance normalization approach in text. Joint factor analysis versus eigenchannels in speaker recognition. This dataset is a well known one and the majority of published papers report results. This was done by analyzing a population of 10 speakers uttering several unique words. Sut submission for nist 2016 speaker recognition evaluation. Test normalization t norm is a score normalization technique that is regularly and successfully applied in the context of textindependent speaker recognition.
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