REPORT Prasad_Idiap-RR-11-2025/IDIAP IDIAP SUBMISSION TO NIST LRE22 LANGUAGE RECOGNITION EVALUATION Prasad, Amrutha Khalil, Driss Madikeri, Srikanth Motlicek, Petr EXTERNAL http://publications.idiap.ch/attachments/reports/2022/Prasad_Idiap-RR-11-2025.pdf PUBLIC Idiap-RR-11-2025 2025 Idiap October 2025 The Idiap submission to the NIST Language Recognition Evaluation (LRE) 2022 consists of three types of systems: (i) Random Forest (RF) and Support Vector Machine (SVM) classifiers trained on embeddings obtained from a pre-trained model from SpeechBrain, (ii) Kaldi-based x-vector-PLDA (Probabilistic Linear Discriminant Analysis) system trained with Kaldi, and (iii) Kaldi-based PLDA trained on the previously mentioned pre-trained model's embeddings. The score-level fusion (that is, linear combination of scores) of the RF and SVM classifiers in (i) was submitted as the primary system for the fixed condition. The score-level fusion of (ii) and (iii) were used as the alternative system. For the open condition, we used two Kaldi-based x-vector PLDA systems with score-level fusion, where additional data from the BABEL corpora was used to train the PLDA models. Our models were developed with Kaldi, PyTorch, SpeechBrain, and Scikit-learn toolkits.