Generative pairwise models for speaker recognition

This paper proposes a simple model for speaker recognition based on i-vector pairs, and analyzes its similarity and differences with respect to the state-of-the-art Probabilistic Linear Discriminant Analysis (PLDA) and Pairwise Support Vector Machine (PSVM) models. Similar to the discriminative PSVM approach, we propose a generative model of i-vector pairs, rather than an usual i-vector based model. The model is based on two Gaussian distributions, one for the "same speakers" and the other for the "different speakers" i-vector pairs, and on the assumption that the i-vector pairs are independent. This independence assumption allows the distributions of the two classes to be independently estimated. The "Two-Gaussian" approach can be extended to the Heavy-Tailed distributions, still allowing a fast closed form solution to be obtained for testing i-vector pairs. We show that this model is closely related to PLDA and to PSVM models, and that tested on the female part of the tel-tel NIST SRE 2010 extended evaluation set, it is able to achieve comparable accuracy with respect to the other models, trained with different objective functions and training procedure

Generative pairwise models for speaker recognition

This paper proposes a simple model for speaker recognition based on i–vector pairs, and analyzes its similarity and differences with respect to the state–of–the–art Probabilistic Linear Discriminant Analysis (PLDA) and Pairwise Support Vector Machine (PSVM) models. Similar to the discriminative PSVM approach, we propose a generative model of i–vector pairs, rather than an usual i–vector based model. The model is based on two Gaussian distributions, one for the “same speakers” and the other for the “different speakers” i–vector pairs, and on the assumption that the i–vector pairs are independent. This independence assumption allows the distributions of the two classes to be independently estimated. The “Two–Gaussian” approach can be extended to the Heavy–Tailed distributions, still allowing a fast closed form solution to be obtained for testing i–vector pairs. We show that this model is closely related to PLDA and to PSVM models, and that tested on the female part of the tel–tel NIST SRE 2010 extended evaluation set, it is able to achieve comparable accuracy with respect to the other models, trained with different objective functions and training procedures

NPLDA: A Deep Neural PLDA Model for Speaker Verification

The state-of-art approach for speaker verification consists of a neural network based embedding extractor along with a backend generative model such as the Probabilistic Linear Discriminant Analysis (PLDA). In this work, we propose a neural network approach for backend modeling in speaker recognition. 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 verification cost. The proposed model, termed as neural PLDA (NPLDA), is initialized using the generative PLDA model parameters. The loss function for the NPLDA model is an approximation of the minimum detection cost function (DCF). The speaker recognition experiments using the NPLDA model are performed on the speaker verificiation task in the VOiCES datasets as well as the SITW challenge dataset. In these experiments, the NPLDA model optimized using the proposed loss function improves significantly over the state-of-art PLDA based speaker verification system.Comment: Published in Odyssey 2020, the Speaker and Language Recognition Workshop (VOiCES Special Session). Link to GitHub Implementation: https://github.com/iiscleap/NeuralPlda. arXiv admin note: substantial text overlap with arXiv:2001.0703

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

Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System

In this paper, we explore the encoding/pooling layer and loss function in the end-to-end speaker and language recognition system. First, a unified and interpretable end-to-end system for both speaker and language recognition is developed. It accepts variable-length input and produces an utterance level result. In the end-to-end system, the encoding layer plays a role in aggregating the variable-length input sequence into an utterance level representation. Besides the basic temporal average pooling, we introduce a self-attentive pooling layer and a learnable dictionary encoding layer to get the utterance level representation. In terms of loss function for open-set speaker verification, to get more discriminative speaker embedding, center loss and angular softmax loss is introduced in the end-to-end system. Experimental results on Voxceleb and NIST LRE 07 datasets show that the performance of end-to-end learning system could be significantly improved by the proposed encoding layer and loss function.Comment: Accepted for Speaker Odyssey 201