Speech spoofing countermeasures based on source voice analysis and machine learning techniques
Raoudha Rahmeni, Anis Ben Aicha, Yassine Ben Ayed · Procedia Computer Science · 2019
Automatic speaker verification (ASV) [7] systems are susceptible to malicious attacks. It discredit the performance of a standard ASV system by increasing its false acceptance rates. This paper presents a new countermeasure for the protection of automatic speaker verification systems from spoofed signals. The new countermeasure is based on the analysis of a sequence of acoustic feature vectors using the glottal inverse filtering. In the proposed method, speech is decomposed into a glottal source signal and model the vocal tract filter through glottal inverse filtering. The IAIF desriptors are constructed and are used as features. Support Vector Machines (SVM) classifier and Extreme learning machine (ELM) are used to classify the obtained features as genuine or spoofed. It is hoped that the proposed method can help to detect the genuine speech from the spoofed one.