| Paper ID | AUD-32.5 |
| Paper Title |
Audio Replay Spoof Attack Detection by Joint Segment-Based Linear Filter Bank Feature Extraction and Attention-Enhanced DenseNet-BiLSTM Network |
| Authors |
Lian Huang, Chi-Man Pun, University of Macau, Macau SAR China |
| Session | AUD-32: Audio for Multimedia and Audio Processing Systems |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Audio and Acoustic Signal Processing: [AUD-SEC] Audio Security |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Most automatic speaker verification (ASV) systems are vulnerable to various spoofing attacks. To address this issue, in this article, we propose a novel model based on attention-enhanced DenseNet-BiLSTM network and segment-based linear filter bank features. First, silent segments are selected from each speech signal by using a short-term zero-crossing rate and energy. If the total duration of silent segments only contains a very limited amount of data, the decaying tails will be selected instead. Second, the linear filter bank features are extracted from the selected segments in the relatively high-frequency domain. Finally, an attention-enhanced DenseNet-BiLSTM architecture which can avoid the problems of overfitting is built. To validate this model, we used two datasets, including BTAS2016 and ASVspoof2017. Experiments show that using the attention-enhanced DenseNet-BiLSTM model with the segment-based linear filter bank feature achieves the best performance. Compared with the baseline system based on constant Q cepstral coefficient and Gaussian mixture model (GMM), the proposed model can produce a relative improvement of 91.68% and 74.04% on the two data sets respectively. |
