Homomorphic Encryption for Speaker Recognition: Protection of Biometric Templates and Vendor Model Parameters

Data privacy is crucial when dealing with biometric data. Accounting for the latest European data privacy regulation and payment service directive, biometric template protection is essential for any commercial application. Ensuring unlinkability across biometric service operators, irreversibility of leaked encrypted templates, and renewability of e.g., voice models following the i-vector paradigm, biometric voice-based systems are prepared for the latest EU data privacy legislation. Employing Paillier cryptosystems, Euclidean and cosine comparators are known to ensure data privacy demands, without loss of discrimination nor calibration performance. Bridging gaps from template protection to speaker recognition, two architectures are proposed for the two-covariance comparator, serving as a generative model in this study. The first architecture preserves privacy of biometric data capture subjects. In the second architecture, model parameters of the comparator are encrypted as well, such that biometric service providers can supply the same comparison modules employing different key pairs to multiple biometric service operators. An experimental proof-of-concept and complexity analysis is carried out on the data from the 2013-2014 NIST i-vector machine learning challenge

Modified-Prior PLDA Based Speaker Recognition System

为减弱注册语音与测试语音时长不一致对说话人识别性能的负面影响,提出一个概率修正PldA建模方法.根据语音时长自适应改变传统PldA模型中I-VECTOr的概率分布函数,提高PldA对每个说话人每段语音的时长表征能力,以增强说话人类别的区分度.为验证基于概率修正PldA模型的有效性,进行了nIST SrE10 COrECOrE测试集在3种不同时长的评测实验,以及nIST 2014 I-VECTOr MACHInE lEArnIng CHAllEngE测试任务.结果表明,相较于传统的PldA训练模型,通过语音时长的约束提高了说话人识别性能.To reduce the negative impact on the performance of speaker recognition systems due to the duration mismatch between enrollment utterance and test utterance,a modified-prior PLDA method is proposed.The probability distribution function of i-vector was modified by incorporating the covariance matrix with duration of each utterance of each speaker during the PLDA training,which further improved the discriminant capability of speaker classification.To evaluate the robustness of the proposed modified-prior PLDA method,extensive experiments were performed on NIST SRE10 core-core task(female part)in duration mismatch conditions and NIST 2014 i-vector machine learning challenge.Experimental results demonstrated that the duration-based modified-prior PLDA method achieved better performance compared with the traditional PLDA.国家自然科学基金资助项目(61105026

Speaker recognition by means of restricted Boltzmann machine adaptation

Restricted Boltzmann Machines (RBMs) have shown success in speaker recognition. In this paper, RBMs are investigated in a framework comprising a universal model training and model adaptation. Taking advantage of RBM unsupervised learning algorithm, a global model is trained based on all available background data. This general speaker-independent model, referred to as URBM, is further adapted to the data of a specific speaker to build speaker-dependent model. In order to show its effectiveness, we have applied this framework to two different tasks. It has been used to discriminatively model target and impostor spectral features for classification. It has been also utilized to produce a vector-based representation for speakers. This vector-based representation, similar to i-vector, can be further used for speaker recognition using either cosine scoring or Probabilistic Linear Discriminant Analysis (PLDA). The evaluation is performed on the core test condition of the NIST SRE 2006 database.Peer ReviewedPostprint (author's final draft

From features to speaker vectors by means of restricted Boltzmann machine adaptation

Restricted Boltzmann Machines (RBMs) have shown success in different stages of speaker recognition systems. In this paper, we propose a novel framework to produce a vector-based representation for each speaker, which will be referred to as RBM-vector. This new approach maps the speaker spectral features to a single fixed-dimensional vector carrying speaker-specific information. In this work, a global model, referred to as Universal RBM (URBM), is trained taking advantage of RBM unsupervised learning capabilities. Then, this URBM is adapted to the data of each speaker in the development, enrolment and evaluation datasets. The network connection weights of the adapted RBMs are further concatenated and subject to a whitening with dimension reduction stage to build the speaker vectors. The evaluation is performed on the core test condition of the NIST SRE 2006 database, and it is shown that RBM-vectors achieve 15% relative improvement in terms of EER compared to i-vectors using cosine scoring. The score fusion with i-vector attains more than 24% relative improvement. The interest of this result for score fusion yields on the fact that both vectors are produced in an unsupervised fashion and can be used instead of i-vector/PLDA approach, when no data label is available. Results obtained for RBM-vector/PLDA framework is comparable with the ones from i-vector/PLDA. Their score fusion achieves 14% relative improvement compared to i-vector/PLDA.Peer ReviewedPostprint (published version