Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems

Voice Processing Systems (VPSes), now widely deployed, have been made significantly more accurate through the application of recent advances in machine learning. However, adversarial machine learning has similarly advanced and has been used to demonstrate that VPSes are vulnerable to the injection of hidden commands - audio obscured by noise that is correctly recognized by a VPS but not by human beings. Such attacks, though, are often highly dependent on white-box knowledge of a specific machine learning model and limited to specific microphones and speakers, making their use across different acoustic hardware platforms (and thus their practicality) limited. In this paper, we break these dependencies and make hidden command attacks more practical through model-agnostic (blackbox) attacks, which exploit knowledge of the signal processing algorithms commonly used by VPSes to generate the data fed into machine learning systems. Specifically, we exploit the fact that multiple source audio samples have similar feature vectors when transformed by acoustic feature extraction algorithms (e.g., FFTs). We develop four classes of perturbations that create unintelligible audio and test them against 12 machine learning models, including 7 proprietary models (e.g., Google Speech API, Bing Speech API, IBM Speech API, Azure Speaker API, etc), and demonstrate successful attacks against all targets. Moreover, we successfully use our maliciously generated audio samples in multiple hardware configurations, demonstrating effectiveness across both models and real systems. In so doing, we demonstrate that domain-specific knowledge of audio signal processing represents a practical means of generating successful hidden voice command attacks

Speaker segmentation and clustering

This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding speaker change points in an audio stream, whereas speaker clustering aims at grouping speech segments based on speaker characteristics. Model-based, metric-based, and hybrid speaker segmentation algorithms are reviewed. Concerning speaker clustering, deterministic and probabilistic algorithms are examined. A comparative assessment of the reviewed algorithms is undertaken, the algorithm advantages and disadvantages are indicated, insight to the algorithms is offered, and deductions as well as recommendations are given. Rich transcription and movie analysis are candidate applications that benefit from combined speaker segmentation and clustering. © 2007 Elsevier B.V. All rights reserved

Multilevel and session variability compensated language recognition: ATVS-UAM systems at NIST LRE 2009

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. J. Gonzalez-Dominguez, I. Lopez-Moreno, J. Franco-Pedroso, D. Ramos, D. T. Toledano, and J. Gonzalez-Rodriguez, "Multilevel and Session Variability Compensated Language Recognition: ATVS-UAM Systems at NIST LRE 2009" IEEE Journal of Selected Topics in Signal Processing, vol. 4, no. 6, pp. 1084 – 1093, December 2010This work presents the systems submitted by the ATVS Biometric Recognition Group to the 2009 Language Recognition Evaluation (LRE’09), organized by NIST. New challenges included in this LRE edition can be summarized by three main differences with respect to past evaluations. Firstly, the number of languages to be recognized expanded to 23 languages from 14 in 2007, and 7 in 2005. Secondly, the data variability has been increased by including telephone speech excerpts extracted from Voice of America (VOA) radio broadcasts through Internet in addition to Conversational Telephone Speech (CTS). The third difference was the volume of data, involving in this evaluation up to 2 terabytes of speech data for development, which is an order of magnitude greater than past evaluations. LRE’09 thus required participants to develop robust systems able not only to successfully face the session variability problem but also to do it with reasonable computational resources. ATVS participation consisted of state-of-the-art acoustic and high-level systems focussing on these issues. Furthermore, the problem of finding a proper combination and calibration of the information obtained at different levels of the speech signal was widely explored in this submission. In this work, two original contributions were developed. The first contribution was applying a session variability compensation scheme based on Factor Analysis (FA) within the statistics domain into a SVM-supervector (SVM-SV) approach. The second contribution was the employment of a novel backend based on anchor models in order to fuse individual systems prior to one-vs-all calibration via logistic regression. Results both in development and evaluation corpora show the robustness and excellent performance of the submitted systems, exemplified by our system ranked 2nd in the 30 second open-set condition, with remarkably scarce computational resources.This work has been supported by the Spanish Ministry of Education under project TEC2006-13170-C02-01. Javier Gonzalez-Dominguez also thanks Spanish Ministry of Education for supporting his doctoral research under project TEC2006-13141-C03-03. Special thanks are given to Dr. David Van Leeuwen from TNO Human Factors (Utrech, The Netherlands) for his strong collaboration, valuable discussions and ideas. Also, authors thank to Dr. Patrick Lucey for his final support on (non-target) Australian English review of the manuscript