Protecting Voice Controlled Systems Using Sound Source Identification Based on Acoustic Cues

Over the last few years, a rapidly increasing number of Internet-of-Things (IoT) systems that adopt voice as the primary user input have emerged. These systems have been shown to be vulnerable to various types of voice spoofing attacks. Existing defense techniques can usually only protect from a specific type of attack or require an additional authentication step that involves another device. Such defense strategies are either not strong enough or lower the usability of the system. Based on the fact that legitimate voice commands should only come from humans rather than a playback device, we propose a novel defense strategy that is able to detect the sound source of a voice command based on its acoustic features. The proposed defense strategy does not require any information other than the voice command itself and can protect a system from multiple types of spoofing attacks. Our proof-of-concept experiments verify the feasibility and effectiveness of this defense strategy.Comment: Proceedings of the 27th International Conference on Computer Communications and Networks (ICCCN), Hangzhou, China, July-August 2018. arXiv admin note: text overlap with arXiv:1803.0915

A Study on Replay Attack and Anti-Spoofing for Automatic Speaker Verification

For practical automatic speaker verification (ASV) systems, replay attack poses a true risk. By replaying a pre-recorded speech signal of the genuine speaker, ASV systems tend to be easily fooled. An effective replay detection method is therefore highly desirable. In this study, we investigate a major difficulty in replay detection: the over-fitting problem caused by variability factors in speech signal. An F-ratio probing tool is proposed and three variability factors are investigated using this tool: speaker identity, speech content and playback & recording device. The analysis shows that device is the most influential factor that contributes the highest over-fitting risk. A frequency warping approach is studied to alleviate the over-fitting problem, as verified on the ASV-spoof 2017 database

Effectiveness in the Realisation of Speaker Authentication

© 2019 IEEE. 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.An important consideration for the deployment of speaker recognition in authentication applications is the approach to the formation of training and testing utterances . Whilst defining this for a specific scenario is influenced by the associated requirements and conditions, the process can be further guided through the establishment of the relative usefulness of alternative frameworks for composing the training and testing material. In this regard, the present paper provides an analysis of the effects, on the speaker recognition accuracy, of various bases for the formation of the training and testing data. The experimental investigations are conducted based on the use of digit utterances taken from the XM2VTS database. The paper presents a detailed description of the individual approaches considered and discusses the experimental results obtained in different cases

Robust Voice Liveness Detection and Speaker Verification Using Throat Microphones

While having a wide range of applications, automatic speaker verification (ASV) systems are vulnerable to spoofing attacks, in particular, replay attacks that are effective and easy to implement. Most prior work on detecting replay attacks uses audio from a single acousticmicrophone only, leading to difficulties in detecting high-end replay attacks close to indistinguishable from live human speech. In this paper, we study the use of a special body-conducted sensor, throat microphone (TM), for combined voice liveness detection (VLD) and ASV in order to improve both robustness and security of ASV against replay attacks.We first investigate the possibility and methods of attacking a TM-based ASV system, followed by a pilot data collection. Second, we study the use of spectral features for VLD using both single-channel and dualchannel ASV systems. We carry out speaker verification experiments using Gaussian mixture model with universal background model (GMM-UBM) and i-vector based systems on a dataset of 38 speakers collected by us. We have achieved considerable improvement in recognition accuracy, with the use of dual-microphone setup. In experiments with noisy test speech, the false acceptance rate (FAR) of the dual-microphone GMM-UBM based system for recorded speech reduces from 69.69% to 18.75%. The FAR of replay condition further drops to 0% when this dual-channel ASV system is integrated with the new dual-channel voice liveness detector.</p