For the plda model, this expression has a closed form solution. In this work we built a lstm based speaker recognition system on a dataset collected from cousera lectures. A plda approach for language and text independent speaker. The theories and practices of speaker recognition are tightly connected in the book. Project description release history download files statistics. Speaker recognition in a multi speaker environment alvin f martin, mark a. The 2016 nist speaker recognition evaluation sre16 is part of an ongoing series of evaluations conducted by nist.
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. Eight subsystems are developed, all based on a stateoftheart approach. Enrollment for speaker identification is textindependent, which means that there are no restrictions on what the speaker says in the audio. Prince, 2007 given a pair of ivectors dw 1,w 2, 1 means two vectors from the same speaker and 0 means two vectors from different speakers. A series of experiments on speaker recognition were conducted to evaluate neural adversarial learning for plda subspace model and plda augmentation model under the same dataset from nist ivector speaker recognition challenge greenberg, banse, doddington, garciaromero, godfrey, kinnunen, martin, mccree, przybocki, reynolds, 2014, chien, chen, 2016, chien, peng, 2017. Unsupervised adaptation of plda models for broadcast. In either case, the sre10 data is only used for the evaluation portion of the setup e. Speaker recognition, however, is a general term and applies to both. These evaluations provide an important contribution to the direction of research efforts and the calibration of technical capabilities. The duration of speech segments has traditionally been controlled in the nist speaker recognition evaluations so that researchers working in this framework have been relieved of the responsibility of dealing with the duration variability that arises in practical applications. If we train our plda with microphone data only, and test with phone data, will it.
In some speaker verification applications the amount of data available for enrolment and verification can be limited. 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. Select the testing console in the region where you created your resource. Ivector transformation and scaling for plda based speaker. 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. If you ought to do some quick experiments there is a python based system for speaker diarization called voiceid it offers both gui. Espywilson, analysis of ivector length normalization in speaker recognition systems, in proc. Moreover, the uncertainty in the ivector estimates should be taken into account in the plda model, due to the short duration of the utterances. Plda, still the performance of speaker recognition is affected under crosssource evaluation condition. Speaker verification also called speaker authentication contrasts with identification, and speaker recognition differs from speaker diarisation recognizing when the same. Due to its origin in textindependent speaker recognition, this paradigm does not make use of the phonetic content of each utterance.
Neural adversarial learning for speaker recognition. The experiments show that the proposed system outperforms the baseline ivector plda approach by relative gains of 31% on female and 9% on male speakers in terms of. In this area, neural networks also contribute with solutions such as 21, 22. The baseline plda model let fi rgr r1, i 2 plda is a generative model that is described by the following equations i. This is an iterative algorithm and consists of 2 steps. Speaker recognition introduction measurement of speaker characteristics construction of speaker models decision and performance applications this lecture is based on rosenberg et al. I think the speaker recognition article explains this well and should have sections for speaker verification and identification. Research group of the 20 summer workshop in the summer of 20, clsp hosted a 4week workshop to explore new challenges in speaker and language recognition. The api can be used to determine the identity of an unknown speaker. However, without access to the target domain data andor any domain mismatch compensation, the domain variability is still a problem in practical applications garciaromero et al.
Letting w ij denote the ivector of the jth utterance session of the ith speaker, the plda model can be. Experiments are presented on nist speaker recognition evaluation sre 2008 and nist sre 2010. Mixture of plda for noise robust ivector speaker verification. Languagesource normalization is an effective technique to reduce language dependency in the stateoftheart ivector plda speaker recognition system mclaren et al. When speaker recognition is used for surveillance applications or in general when the subject is not aware of it then the common privacy concerns of identifying unaware subjects apply.
Textdependent speaker recognition using plda with uncertainty propagation t. Channel compensation for speaker recognition using map. The experimental protocol and corresponding results are given in section 3 and section 4. Languagesource normalization is an effective technique to reduce language dependency in the stateoftheart ivectorplda speaker recognition system mclaren et al. This can be accomplished by using the language label to identify different. The term voice recognition can refer to speaker recognition or speech recognition. One of the aims of this paper is to study the impact of the volume of enrolment and verification data on the performance of the system. The likelihood ratio score of the generative plda model is posed as a discriminative similarity function and the learnable parameters of the score function are optimized using a veri. It consists of 392 hours of conversational telephone speech in english, arabic, mandarin chinese, russian and spanish and associated english transcripts used as training data in.
Snrmulticonditon approaches of robust speaker model compensation based on plda in practical environment xin li 1, lei wang, and jie zhu 1department of electronic engineering, shanghai jiao tong university, shanghai, china regular research paper abstract in this paper, we focus on the research of robust speaker verification system in adverse noise conditions. A group of 16 international researchers came together to collaborate in a set of research areas described below. Towards pldarbm based speaker recognition in mobile. Plda for speaker verification with utterances of arbitrary. Chapter in book, report or conference volume conference contribution. The traditional speaker recognition approach entails using ivectors 3 and probabilistic linear discriminant analysis plda 5. The workshop was motivated by the successful outcomes of the 2008. Discriminative multidomain plda for speaker verification alexey sholokhov 1. Speaker recognition for forensic applications this work was sponsored under air force contract fa872105c0002. The experiments show that the proposed system outperforms the baseline ivectorplda approach by relative gains of 31% on female and 9% on male speakers in terms of. Deep learning is progressively gaining popularity as a viable alternative to ivectors for speaker recognition.
Combined with length normalization, plda has delivered stateoftheart performance in speaker veri. Analysis of the influence of speech corpora in the plda. Part of the lecture notes in computer science book series lncs, volume 8082. Original speaker recognition systems used the average output of several analog filters to perform matching, often with the aid of humans in the loop. Plda in the isupervector space for textindependent. These are the joint factor analysis, its modified version called the concept of ivectors, and the probabilistic linear discriminant analysis plda. Introduction measurement of speaker characteristics. The technical problems are rigorously defined, and a complete picture is made of the relevance of the discussed algorithms and their usage in building a comprehensive. Speaker recognition is unobtrusive, speaking is a natural process so no unusual actions are required. Textindependent speaker verification recognizing speakers regardless of content and nonparallel voice conversion transforming voice identities. In the estep, an expectation of the log likelihood of the training adapation data given the current gmm is computed. A plda approach for language and text independent speaker recognition abbas khosravani 1, mohammad mehdi homayounpour 1, dijana petrovskadelacr eta.
Dec 27, 2019 speaker recognition stateoftheart techniques are usually considered for these representations, including gaussian mixture models, jfa, ivectors, and plda. The influence of development data is analyzed utilizing distinct speech corpora. Speaker recognition or broadly speech recognition has been an active area of research for the past two decades. Speaker recognition is a pattern recognition problem. The speaker s voice is recorded, and a number of features are extracted to form a unique voiceprint. Experiments are carried out on the mobio sre database, which is a challenging and publicly available dataset for mobile speaker recognition with limited amounts of training data. Snrmulticonditon approaches of robust speaker model. Speaker recognition indian institute of technology guwahati. Pldabased speaker recognition stateoftheart speaker recognition techniques rely on generative pairwise models 8. When plda is used on an ivector, dimension reduction.
The proposed model, termed as neural plda nplda, is initialized using the generative plda model parameters. Deep learning for ivector speaker and language recognition. On behaviour of plda models in the task of speaker recognition. Fundamentals of speaker recognition introduces speaker identification, speaker verification, speaker audio event classification, speaker detection, speaker tracking and more. Curriculum learning based approaches for noise robust. Experiments on the core test condition 5 of the nist speaker recognition. Introduction for more than a decade, speaker recognition researchers have en. However, in real environments the variant noise will. Pdf plda based speaker recognition on short utterances. Speaker recognition is the identification of a person from characteristics of voices. In particular, it can be shown 17, 18 that it is a quadratic form that can. Recently, dnnivector plda systems have been shown to perform well in most speaker recognition tasks. Plda, still the performance of speaker recognition is affected under crosssource.
This is the official location of the kaldi project. Review and cite speaker recognition protocol, troubleshooting and other. Speaker recognition is a very active research area with notable applications in various fields such as biometric authentication, forensics, security, speech recognition, and speaker diarization, which has contributed to steady interest towards this discipline. Introduction for more than a decade, speaker recognition researchers have enjoyed the availability of large quantities of speech data provided by the national institute of standards and technologies through their speaker recognition evaluations nist sres. They are intended to be of interest to all researchers working on the general problem of text. Speaker recognition known as voiceprint recognition in industry is the process of. Watching her face light up, or frown, or wince, or take notes, was one of the smartest things ive done as a speaker. The plda model assumes that the ivectors are distributed according to the standard normal distribution, whereas it is well known that this is not the case. Plda based speaker recognition on short utterances qut eprints. It can be implemented by extending either snwccn or snlda mclaren and van leeuwen, 2012 in order to mitigate variations that separate languages. 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. Plda based speaker recognition on short utterances. An accurate estimation of speaker and channel subspaces from a multilin.
Przybocki national institute of standards and technology gaithersburg, md 20899 usa alvin. Not only forensic analysts but also ordinary persons will bene. The speakers voice is recorded, and a number of features are extracted to form a unique voiceprint. Source normalization technique which was developed to compensate for. We assume that the phrase labels are given for all three kinds of utterances, i. Speaker recognition introduction speaker, or voice, recognition is a biometric modality that uses an individuals voice for recognition purposes. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the united states government.
Promising results have been recently obtained with convolutional neural networks cnns when fed by raw speech samples directly. Speaker and language recognition center for language and. Oct 01, 20 if you ought to do some quick experiments there is a python based system for speaker diarization called voiceid it offers both gui. This paper proposes a density model transformation for speaker recognition systems based on ivectors and probabilistic linear discriminant analysis plda classification. Speaker recognition stateoftheart techniques are usually considered for these representations, including gaussian mixture models, jfa, ivectors, and plda. The existing applications in voiceactivated embedded systems solve the problem of recognition of the spoken words only or the problem of recognition of a speaker through the words uttered only. It is shown that it is preferable to cluster available speech corpora to classes, train one plda model for each class and fuse the results at the end. It begins with an initial gmm with even random parameters. Plda for speaker verification with utterances of arbitrary duration abstract.
The speaker recognition perspective enables readers to apply machine learning techniques to address practical issues e. If you already have data you want to use for enrollment and testing, and you have access to the training data e. The goal of this project, therefore, is the development of a robust algorithm for both speech recognition and speaker verification. Improving plda speaker verification performance using. In this paper, we advocate the use of the uncompressed form of ivector and depend on subspace modeling using probabilistic linear discriminant analysis plda in handling the speaker and session or channel variability. A language independent plda training algorithm has been proposed to improve performance of textindependent speaker recognition under multilingual trial condition. Improving plda speaker verification performance using domain. Plda is an extension of the linear discriminative analysis lda, by introducing a gaussian prior on the mean vector of classes. This framework can be decomposed into three stages 4. Speaker recognition has been a widely used field topic of speech. An ivector is a lowdimensional vector containing both speaker and channel information acquired from a speech segment. Speaker recognition in a multispeaker environment alvin f martin, mark a. I merged the stub article voice biometrics here in order to avoid content forking. In general, experiments carried out using this combined strategy employ linear discriminant analysis lda after the ivector.
Speaker recognition from raw waveform with sincnet deepai. Kenny, 2010 the verification score is computed for all possible modeltest ivector. Nov 20, 2014 nicole mintiens is a marketer, an upandcoming public speaker, and my fiancee. It has been predicted that telephonebased services with integrated speech recognition, speaker recognition, and. Curriculum learning based approaches for noise robust speaker. The proposed approach take advantageous of multilingual utterances by bilingual speakers to improve speaker recognition in multilingual scenarios. Plda in the isupervector space for textindependent speaker. This study aims at proposing a languageindependent plda training algorithm in order to reduce the effect of language on the performance of speaker recognition. The second aim is focused on the improvement of the speaker verification using plda. Plda speaker verification with limited speech data. Citescore values are based on citation counts in a given year e. For example, in call center applications, the mismatch between the outdomain and indomain data arises. Speaker recognition from raw waveform with sincnet.