Subband Channel Selection using TEO for Replay Spoof Detection in Voice Assistants
Harsh Kotta, Ankur T. Patil, Rajul Acharya, Hemant A. Patil · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference · 2020
Recently, there is an increase in the demand for Voice Assistants (VAs) due to their convenience in accessing and controlling the household devices. To make VAs user-friendly, less strict speaker verification constraints are imposed onto them which makes VAs highly vulnerable to spoofing attacks. In this paper, authors propose the design of front-end countermeasure system against replay spoofing attack for VAs that make use of microphone array to capture spatial diversity. We exploit this microphone array information by proposing a novel approach of the subband channel selection using mathematical structure of Teager Energy Operator (TEO). These selected subband channels are used to compute proposed Teager Energy Cepstral Coefficients (TECC max ) feature set. With this approach, we gain significant improvement in the performance of replay attack detection task on VAs against the baseline feature set, i.e., Constant-Q Cepstral Coefficient (CQCC). Results indicate an absolute reduction in Equal Error Rate (EER) of 4.11% and 8.66% on development and evaluation set, respectively, of ReMASC dataset. Authors also performed classffier-level fusion of GMM, and LCNN-based back end classifiers using proposed TECC max feature set and obtained absolute reduction of 5.98% and 10.67% on development and evaluation sets, respectively.