We should note that all ivectors of the test set must be whitened. The ivectors are smaller in size to reduce the execution time of the recognition task while maintaining. A language independent plda training algorithm has been proposed to improve performance of textindependent speaker recognition under multilingual trial condition. In this work we investigate the application of one of these techniques supervised plda map adaptation 6 to adapting a telephony speaker recognition system to microphone channel speech. Introduction automatic speaker recognition technology aims to distinguish the target speaker and the imposter by two main processing. We assume that the phrase labels are given for all utterances in plda training, speaker enrollment and testing.
Deep neural network approaches to speaker and language. The gaussian plda model assumes that the ivectors are distributed according. Discriminative scoring for speaker recognition based on i. Despite the application of dnn are very successful in automatic speech recognition asr field, a direct transition to speaker recognition is much more challenging. Speaker recognition stateoftheart techniques are usually considered for these representations, including gaussian mixture models, jfa, ivectors, and plda. Local training for plda in speaker verification arxiv. The gaussian plda model assumes that the ivectors are distributed according to the standard normal distribution. In this area, neural networks also contribute with solutions such as 21, 22. Ivectorplda variants for textdependent speaker recognition. Key method in this paper, we propose a system that incorporates probabilistic linear discriminant analysis plda for ivector scoring, a method already frequently utilized in speaker recognition tasks, and uses unsupervised calibration of the plda scores to determine the clustering stopping.
Endtoend dnn based speaker recognition inspired by ivector. Plda based speaker recognition on short utterances core. Introduction the impressive gains in performance obtained using deep neural networks dnns for automatic speech recognition asr 1 have motivated the application of dnns to other speech technologies such as speaker recognition sr and language recognition lr 210. Speaker recognition with random digit strings using. Mixture of plda models in ivector space for gender. Also, research has proven that it is possible to recover biometric samples from templates for other modalities such as. In past studies, neural networks have been investigated for speaker recognition 11, 12. Nowadays, factor analysis based techniques become part of stateoftheart speaker recognition sr systems. Pdf ivector feature representation with probabilistic linear discriminant analysis plda scoring in speaker recognition system has recently. Probabilistic linear discriminant analysis plda with. These are the joint factor analysis, its modified version called the concept of ivectors, and the probabilistic linear discriminant analysis plda. 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. This paper proposes to estimate parametric nonlinear transformations of ivectors for speaker recognition systems based on probabilistic linear discriminant analysis plda classification. It not only includes several existing supervised and unsupervised domain adaptation methods but also makes possible more flexible usage of available data in.
Ivector plda variants for textdependent speaker recognition t. A plda approach for language and text independent speaker recognition abbas khosravani 1, mohammad mehdi homayounpour 1, dijana petrovskadelacr eta. Introduction the series of nist speaker recognition evaluations 1 has had a strong in. Pdf plda based speaker recognition on short utterances. In this paper, we apply and enhance the ivectorplda paradigm to textdependent speaker recognition. A plda approach for language and text independent speaker recognition. A big part of this improvement has been the availability of large quantities of speakerlabeled data from telephone recordings.
Pldabased speaker recognition stateoftheart speaker recognition techniques rely on generative pairwise models 8. Modifiedprior plda and score calibration for duration. Plda based speaker recognition on short utterances qut. A generalized framework for domain adaptation of plda in. Section 2 describes the training, development and evaluation data. Pdf compensating interdataset variability in plda hyper. This paper studies the problem of speaker recognition for multispeaker conversations using a modern dnn embeddingbased system. Plda for speaker verification with utterances of arbitrary duration. Matejka, and lukas burget, endtoend dnn based speaker recognition inspired by ivector and plda, arxiv eprints arxiv. This paper proposes a generalized framework for domain adaptation of probabilistic linear discriminant analysis plda in speaker recognition. This paper studies the problem of speaker recognition for multi speaker conversations using a modern dnn embeddingbased system.
In speaker or face recognition, plda factorizes the variability of the observations for a. Pdf there are many factors affecting the variability of an ivector extracted from a speech segment such as the acoustic con tent, segment duration. This package contains scripts that run the fast and scalable plda 1 and twostage plda 2. Channel compensation for speaker recognition using map. Plda based speaker recognition on short utterances qut eprints. Introduction speaker recognition accepts or rejects a claimed identity of a speaker based on speech input.
This study aims at proposing a languageindependent plda training algorithm in order to reduce the effect of language on the performance of speaker recognition. In 1, the ivector features were tested on the 2008 nist speaker recognition evaluation sre telephone data. Analysis of ivector length normalization in speaker. 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. Apr 18, 2018 1 anna silnova, mireia diez, oldrich plchot, pavel matejka, lukas burget, endtoend dnn based speaker recognition inspired by ivector and plda, ieee sigport, 2018. Textdependent speaker recognition using plda with uncertainty propagation t. The experimental protocol and corresponding results are given in section 3 and section 4.
It not only includes several existing supervised and unsupervised domain adaptation methods but also makes possible more flexible usage of available data in different domains. Unsupervised adaptation of plda models for broadcast. In this paper, we apply and enhance the ivector plda paradigm to textdependent speaker recognition. Due to its origin in textindependent speaker recognition, this paradigm does not make use of the phonetic content of each utterance. Moreover, the uncertainty in the ivector estimates should be taken into account in the plda model, due to the short duration of the utterances. Stateoftheart speaker recognition for telephone and video. Plda baseline on both long and short duration utterances. Speaker recognition until recently, most stateoftheart speaker recognition systems were based on ivectors 2. On behaviour of plda models in the task of speaker recognition. Plda which is closely related to joint factor analysis jfa 15 used for speaker recognition is a probabilistic extension of linear discriminant analysis lda. The dnns most often found in speaker recognition are trained as acoustic models for automatic speech recognition asr, and are then used to enhance phonetic modeling in.
Proceedings of the speaker and language recognition workshop. Over several decades, speaker recognition performance has steadily improved for applications using telephone speech. Languageaware plda for multilingual speaker recognition. Introduction the earliest successful approach to speaker recognition used the gaussian mixture modeling gmm from the training data followed by an adaptation using maximumaposteriori map rule 1. Plda, still the performance of speaker recognition is affected under crosssource. The vectors in the lowdimensional space are called ivectors. The proposed model, termed as neural plda nplda, is initialized using the generative plda model parameters. Indexterms xvectors, plda, neural plda, soft detection cost, speaker veri. Idvc compensates dataset shifts in the ivector space by constraining the shifts to a low.
Recently we have introduced a method named interdataset variability compensation idvc in the context of speaker recognition in a mismatched dataset. Deep learning for ivector speaker and language recognition. An ivector extractor suitable for speaker recognition with. G plda model introduced in 3 then assumes that each ivector can be decomposed as 2 in the jargon of speaker recognition, t he model comprises two parts. The well known ivector representation of speech segments has the convenient property. I vector transformation and scaling for plda based speaker recognition sandro cumani and pietro laface fsandro. Recent work has shown that deep neural networks can be. In order to do speaker verification, the embeddings are extracted and used in a standard backend, e.
Plda, still the performance of speaker recognition is affected under crosssource evaluation condition. Besides the original formulation in 7, there are other. Pdf ivectorplda speaker recognition using support vectors with. A matlab toolbox for speaker recognition research version 1. Stc speaker recognition system for the nist i vector. Nonlinear ivector transformations for pldabased speaker. Eight subsystems are developed, all based on a stateoftheart approach.
The system combining ivector and probabilistic linear discriminant analysis plda has been applied with great success in the speaker recognition task. Mar 25, 2015 this package contains scripts that run the fast and scalable plda 1 and twostage plda 2. The proposed approach take advantageous of multilingual utterances by bilingual speakers to improve speaker recognition in multilingual scenarios. Ideally the nns should however be trained directly for the speaker verification task, i. Plda subsystem among stateoftheart speaker verification systems, leading positions are occupied by plda systems 3,4, working in the. The ivector space gives a lowdimensional representation of a speech segment and training data of a plda model, which offers greater robustness under different conditions. Index termsmultilingual speaker recognition, ivector, plda i. The dnns most often found in speaker recognition are trained as acoustic models for automatic speech recognition asr, and are then used to enhance phonetic modeling in the ivector ubm. Fullposterior plda in speaker recognition technical. The availability of more than one enrollment utterance for a speaker allows a variety of con. A plda approach for language and text independent speaker. The ivectorplda technique and its variants have also been successfully used in textdependent speaker recognition tasks 8, 9, 10. Deep discriminant analysis for ivector based robust speaker. I vector transformation and scaling for plda based speaker.
This is a big advantage of plda in speaker recognition, since in most situations only very few utterances are available for enrollment. Deep neural networks for small footprint textdependent. In this study, we use plda to transform speaker characteristics in the ivector space. A plda model for textdependent speaker recognition in this section, we describe the phrase dependent version of plda which we used in experimenting with the rsr data. Plda based speaker recognition on short utterances by ahilan kanagasundaram, robert j. Discriminative scoring for speaker recognition based on ivectors. Gplda model introduced in 3 then assumes that each ivector can be decomposed as 2 in the jargon of speaker recognition, t he model comprises two parts.
An accurate estimation of speaker and channel subspaces from a multilin. Speaker diarization via unsupervised ivector clustering has gained popularity in recent years. A big part of this improvement has been the availability of large quantities of speaker labeled data from telephone recordings. In these evaluations, the canonical speaker detection task has always prescribed trials.
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