Petr Grinberg

Traditional anti-spoofing systems cannot be used straightforwardly with streaming audio because they are designed for finite utterances. Such offline models can be applied in streaming with the help of buffering; however, they are not effective in terms of memory and computational consumption. We propose a novel approach called RawSpectrogram that makes offline models streaming-friendly without a significant drop in quality. The method was tested on RawNet2 and AASIST, resulting in new architectures called RawRNN (RawLSTM and RawGRU), RS-AASIST, and TAASIST. The RawRNN-type models are much smaller and achieve a better Equal Error Rate than their base architecture, RawNet2. RS-AASIST and TAASIST have fewer parameters than AASIST and achieve similar quality. We also proved our concept for models with time-frequency transform front-ends and automatic speaker verification systems by proposing RECAPA-TDNN based on ECAPA-TDNN. RS-AASIST and RECAPA-TDNN were combined into the first streaming-friendly spoofing-aware speaker verification system reported in the literature. This joint system achieves significantly better quality than the corresponding offline solutions. All our models require far fewer floating-point operations for score updates. RawSpectrogram usage significantly reduces the latency of the prediction and allows the system to update the probability with each new chunk from the stream, preserving all information from the past. To the best of our knowledge, TAASIST is the most successful voice anti-spoofing system that employs a vanilla Transformer trained using supervised learning.

Automatic Speaker Verification (ASV) system is a type of bio-metric authentication. It can be attacked by an intruder, who falsifies data in order to get access to protected information. Countermeasures (CM) are special algorithms that detect these spoofing-attacks. While the ASVspoof Challenge series were focused on the development of CM for fixed ASV system, the new Spoofing Aware Speaker Verification (SASV) Challenge organizers believe that best results can be achieved if CM and ASV systems are optimized jointly. One of the approaches for cooperative optimization is a fusion over embeddings or scores obtained from ASV and CM models. The baselines of SASV Challenge 2022 present two types of fusion: score-sum and back-end ensemble with a 3-layer MLP. This paper describes our research of other fusion methods, including boosting over embeddings, which has not been used in anti-spoofing studies before.