2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | SPE-26.1 | ||
| Paper Title | DATA AUGMENTATION WITH SIGNAL COMPANDING FOR DETECTION OF LOGICAL ACCESS ATTACKS | ||
| Authors | Rohan Kumar Das, Jichen Yang, Haizhou Li, National University of Singapore, Singapore | ||
| Session | SPE-26: Speaker Verification Spoofing and Countermeasures | ||
| Location | Gather.Town | ||
| Session Time: | Wednesday, 09 June, 15:30 - 16:15 | ||
| Presentation Time: | Wednesday, 09 June, 15:30 - 16:15 | ||
| Presentation | Poster | ||
| Topic | Speech Processing: [SPE-SPKR] Speaker Recognition and Characterization | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | The recent advances in voice conversion (VC) and text-to-speech (TTS) make it possible to produce natural sounding speech that poses threat to automatic speaker verification (ASV) systems. To this end, research on spoofing countermeasures has gained attention to protect ASV systems from such attacks. While the advanced spoofing countermeasures are able to detect known nature of spoofing attacks, they are not that effective under unknown attacks. In this work, we propose a novel data augmentation technique using a-law and mu-law based signal companding. We believe that the proposed method has an edge over traditional data augmentation by adding small perturbation or quantization noise. The studies are conducted on ASVspoof 2019 logical access corpus using light convolutional neural network based system. We find that the proposed data augmentation technique based on signal companding outperforms the state-of-the-art spoofing countermeasures showing ability to handle unknown nature of attacks. | ||