While the technologies empowering malicious audio deepfakes have dramatically evolved in recent years due to generative AI advances, the same cannot be said of global research into spoofing (deepfake) countermeasures. This paper highlights how current deepfake datasets and research methodologies led to systems that failed to generalize to real world application. The main reason is due to the difference between raw deepfake audio, and deepfake audio that has been presented through a communication channel, e.g. by phone. We propose a new framework for data creation and research methodology, allowing for the development of spoofing countermeasures that would be more effective in real-world scenarios. By following the guidelines outlined here we improved deepfake detection accuracy by 39

This manuscript describes the I4U submission to the 2020 NIST Speaker Recognition Evaluation (SRE'20) Conversational Telephone Speech (CTS) Challenge. The I4U's submission was resulted from active collaboration among researchers across eight research teams - I^2R (Singapore), UEF (Finland), VALPT (Italy, Spain), NEC (Japan), THUEE (China), LIA (France), NUS (Singapore), INRIA (France) and TJU (China). The submission was based on the fusion of top performing sub-systems and sub-fusion systems contributed by individual teams. Efforts have been spent on the use of common development and validation sets, submission schedule and milestone, minimizing inconsistency in trial list and score file format across sites.

Chapter Contents: 8.1 Automated password reset: an example of a commercial application using voice biometrics 8.1.1 Overview 8.1.2 Introduction 8.1.3 System architecture 8.1.4 Voice biometric system 8.1.5 Summary 8.2 Testing of commercial voice biometric systems 8.2.1 Introduction 8.2.1.1 Biometric testing 8.2.2 User analysis 8.2.3 Summary 8.3 Forensic speaker recognition 8.3.1 Introduction 8.3.2 Forensic speaker recognition and the strength of evidence 8.3.3 The forensic experts workflow 8.3.4 Technical challenges 8.3.4.1 Improving interpretability of scores 8.3.4.2 Score normalization 8.3.4.3 Score calibration 8.3.4.4 Condition adaptation 8.3.4.5 Dealing with multi-speaker recordings 8.3.5 Training—communication between system developers and end-users 8.3.6 Conclusions References

This paper describes the Nuance-Politecnico di Torino (NPT) speaker recognition system submitted to the NIST SRE12 evaluation campaign.Included are the results of postevaluation tests, focusing on the analysis of the effects of score normalization and condition-dependent calibration.The submitted system combines the results of five acoustic recognizers all based on Gaussian Mixture Models (GMMs).Each system has its own front end, with features differing by their type and dimension.We illustrate the process of development data selection and configuration of state-of-the art technology, which contributed to obtaining good performance in all the test conditions proposed in this evaluation.

This paper describes the Loquendo – Politecnico di Torino system evaluated on the 2006 NIST speaker recognition evaluation dataset. This system was among the best participants in this evaluation. It combines the results of two independent GMM systems: a Phonetic GMM and a classical GMM. Both systems rely on an intersession variation compensation approach, performed in the feature domain. It allowed a 30% error rate reduction with respect to our 2005 system. The linear combination of the two GMM engines gives a further 10% error rate reduction. We also report the results of a set of post evaluation experiments, related to the training data for the intersession variation evaluation, both for the telephone and microphone datasets. The approach adopted for the two wire tests is also described, showing the effect of the speaker segmentation component of our system. Finally, we describe how we performed the incremental unsupervised adaptation tests.

In this paper, we propose a word confidence measure based on phone durations depending on large contexts. The measure is based on the expected duration of each recognized phone in a word. In the approach here proposed the duration of each phone is in principle context-dependent, and the measure is a function of the distance between the observed and expected phone duration distributions within a word. Our experiments show that, since the “duration confidence” does not make use of any acoustic information, its Equal Error Rate (EER) in terms of False Accept and False Rejection rates is not as good as the one obtained by using the more informed acoustic confidence measure. However, combining the two measures by a simple linear interpolation, the system EER improves by 6% to 10% relative on an isolated word recognition task in several languages.

This paper describes the experimental setup and the results obtained using several state-of-the-art speaker recognition classifiers.The comparison of the different approaches aims at the development of real world applications, taking into account memory and computational constraints, and possible mismatches with respect to the training environment.The NIST SRE 2008 database has been considered our reference dataset, whereas nine commercially available databases of conversational speech in languages different form the ones used for developing the speaker recognition systems have been tested as representative of an application domain.Our results, evaluated on the two domains, show that the classifiers based on i-vectors obtain the best recognition and calibration accuracy.Gaussian PLDA and a recently introduced discriminative SVM together with an adaptive symmetric score normalization achieve the best performance using low memory and processing resources.

This paper describes the improvement introduced in the Loquendo-Politecnico di Torino (LPT) speaker recognition system submitted to the NIST SRE10 evaluation campaign. This system combines the results of eight core acoustic systems all based on Gaussian Mixture Models (GMMs). We illustrate the key factors, in the selection of the development data and in engineering state-of-the art technology, which contributed to the very good performance and calibration of our system in all the test conditions proposed in this evaluation.

This paper compares the performance of large scale Support Vector Machine training algorithms tested on a language recognition task. We analyze the behavior of five SVM approaches for training phonetic and acoustic models, and we compare their performance in terms of number of iterations to reach convergence, training time and scalability towards large databases. Our results show that the accuracy of these algorithms is asymptotically equivalent, but they have different behavior with respect to the time required to converge. Some of these algorithms not only scale linearly with the training set size, but are also able to give their best results after just a few iterations on the database.

This paper describes the system submitted by Loquendo and Politecnico di Torino (LPT) for the 2009 NIST Language Recognition Evaluation. The system is a combination of classifiers based on two core acoustic models and on two core phone tokenizers. It exploits several state-of-the-art techniques that have been successfully applied in recent years both in speaker and in language recognition.

Language recognition systems based on acoustic models reach state of the art performance using discriminative training techniques. In speaker recognition, eigenvoice modeling of the speaker, and the use of speaker factors as input features to SVMs has recently been demonstrated to give good results compared to the standard GMM-SVM approach, which combines GMMs supervectors and SVMs. In this paper we propose, in analogy to the eigenvoice modeling approach, to estimate an eigen-language space, and to use the language factors as input features to SVM classifiers. Since language factors are low-dimension vectors, training and evaluating SVMs with different kernels and with large training examples becomes an easy task. This approach is demonstrated on the 14 languages of the NIST 2007 language recognition task, and shows performance improvements with respect to the standard GMM-SVM technique.

In this paper, we propose a word confidence measure based on phone durations depending on large contexts. The measure is based on the expected duration of each recognized phone in a word. In the approach here proposed the duration of each phone is in principle context-dependent, and the measure is a function of the distance between the observed and expected phone duration distributions within a word. Our experiments show that, since the "duration confidence" does not make use of any acoustic information, its Equal Error Rate (EER) in terms of False Accept and False Rejection rates is not as good as the one obtained by using the more informed acoustic confidence measure. However, combining the two measures by a simple linear interpolation, the system EER improves by 6% to 10% relative on an isolated word recognition task in several languages.

This paper describes the improvements introduced in the Loquendo-Politecnico di Torino (LPT) speaker recognition system submitted to the NIST SRE08 evaluation campaign. This system, which was among the best participants in this evaluation, combines the results of three core acoustic systems, two based on Gaussian Mixture Models (GMMs), and one on Phonetic GMMs. We discuss the results of the experiments performed for the 10sec-10sec condition and for the core condition, including the challenging tasks involving a target speaker and an interviewer. The error rate reduction of our SRE08 system compared to the SRE06 system ranges from 25% of the telephone-interview condition to 57% of the interview-interview condition. On the test with telephone and microphone conversations, the improvements range from 9% to 32%.

This paper presents a stream-based approach for unsupervised multi-speaker conversational speech segmentation.The main idea of this work is to exploit prior knowledge about the speaker space to find a low dimensional vector of speaker factors that summarize the salient speaker characteristics.This new approach produces segmentation error rates that are better than the state of the art ones reported in our previous work on the segmentation task in the NIST 2000 Speaker Recognition Evaluation (SRE). We also show how the performance of a speaker recognition system in the core test of the 2006 NIST SRE is affected, comparing the results obtained using single speaker and automatically segmented test data.

This paper describes the system submitted by Politecnico di Torino for the 2007 NIST Language Recognition Evaluation. The system, which was among the best participants in this evaluation, is a combination of classifiers based on three acoustic models and on two sets of Parallel Phone tokenizers. It exploits several state-of-the-art techniques that have been successfully applied in recent years both in speaker and in language recognition.We illustrate the models, the classification techniques and the performance of the system components, and of their combination, in the NIST-07 close-set 30 sec General Language Recognition task. We also highlight the difficulties in setting appropriate decision thresholds whenever the training data of a language are scarce, or the test data are collected through previously unseen channels.

Gaussian Mixture Models (GMMs) in combination with Support Vector Machine (SVM) classifiers have been shown to give excellent classification accuracy in speaker recognition. In this work we use this approach for language identification, and we compare its performance with the standard approach based on GMMs. In the GMM-SVM framework, a GMM is trained for each training or test utterance. Since it is difficult to accurately train a model with short utterances, in these conditions the standard GMMs perform better than the GMM-SVM models. To overcome this limitation, we present an extremely fast GMM discriminative training procedure that exploits the information given by the separation hyperplanes estimated by an SVM classifier. We show that our discriminative GMMs provide considerable improvement compared with the standard GMMs and perform better than the GMM-SVM approach for short utterances, achieving state of the art performance for acoustic only systems.