Constant Q cepstral coefficients: A spoofing countermeasure for automatic speaker verification
نویسندگان
چکیده
Recent evaluations such as ASVspoof 2015 and the similarly-named AVspoof have stimulated a great deal of progress to develop spoofing countermeasures for automatic speaker verification. This paper reports an approach which combines speech signal analysis using the constant Q transform with traditional cepstral processing. The resulting constant Q cepstral coefficients (CQCCs) were introduced recently and have proven to be an effective spoofing countermeasure. An extension of previous work, the paper reports an assessment of CQCCs generalisation across three different databases and shows that they deliver state-of-the-art performance in each case. The benefit of CQCC features stems from a variable spectro-temporal resolution which, while being fundamentally different to that used by most automatic speaker verification system front-ends, also captures reliably the tell-tale signs of manipulation artefacts which are indicative of spoofing attacks. The second contribution relates to a cross-database evaluation. Results show that CQCC configuration is sensitive to the general form of spoofing attack and use case scenario. This finding suggests that the past single-system pursuit of generalised spoofing detection may need rethinking.
منابع مشابه
A New Feature for Automatic Speaker Verification Anti-Spoofing: Constant Q Cepstral Coefficients
Efforts to develop new countermeasures in order to protect automatic speaker verification from spoofing have intensified over recent years. The ASVspoof 2015 initiative showed that there is great potential to detect spoofing attacks, but also that the detection of previously unforeseen spoofing attacks remains challenging. This paper argues that there is more to be gained from the study of feat...
متن کاملFeature Selection Based on CQCCs for Automatic Speaker Verification Spoofing
The ASVspoof 2017 challenge aims to assess spoofing and countermeasures attack detection accuracy for automatic speaker verification. It has been proven that constant Q cepstral coefficients(CQCCs) processes speech in different frequencies with variable resolution and performs much better than traditional features. When coupled with a Gaussian mixture model (GMM), it is an excellently effective...
متن کاملSUT System Description for Anti-Spoofing 2017 Challenge
Reliability of Automatic Speaker Verification (ASV) systems has always been a concern in dealing with spoofing attacks. Among these attacks, replay attack is the simplest and the easiest accessible method. This paper describes a replay spoofing detection system applied to ASVspoof2017 corpus. To reach this goal, features such as Constant-Q Cepstral Coefficients (CQCC), Modified Group Delay (MGD...
متن کاملDevelopment of CRIM system for the automatic speaker verification spoofing and countermeasures challenge 2015
The automatic speaker verification spoofing and countermeasures challenge 2015 provides a common framework for the evaluation of spoofing countermeasures or anti-spoofing techniques in the presence of various seen and unseen spoofing attacks. This contribution proposes a system consisting of amplitude, phase, linear prediction residual, and combined amplitude phase-based countermeasures for the...
متن کاملDetection of Replay Attacks Using Single Frequency Filtering Cepstral Coefficients
Automatic speaker verification systems are vulnerable to spoofing attacks. Recently, various countermeasures have been developed for detecting high technology attacks such as speech synthesis and voice conversion. However, there is a wide gap in dealing with replay attacks. In this paper, we propose a new feature for replay attack detection based on single frequency filtering (SFF), which provi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Computer Speech & Language
دوره 45 شماره
صفحات -
تاریخ انتشار 2017