ASVSpoof is a series of community-led challenges aimed at advancing the development of robust automatic speaker verification (ASV) systems and anti-spoofing countermeasures (CM). The fifth edition of the challenge focuses on speech deepfakes and features two tracks: Track 1: Robust Speech Deepfake Detection (DF) and Track 2: Spoofing-Robust Automatic Speaker Verification (SASV). In this report, we describe in detail the system submitted by the IDVoice team to the open condition of the SASV track (Track 2). Our solution is a score-level fusion of independently trained CM and ASV systems. The CM system is composed of six neural networks of four distinct architectures, while the ASV system is a ResNet-based model. Our final submission achieves a 0.1156 min a-DCF on the challenge evaluation set.