Abstract: We present our submission to the DCASE 2020 Challenge Task 2, which aims to promote research in anomalous sound detection. We found that a speaker recognition approach enables the use of all the training data, even from different machine types, to detect anomalies in specific machines. Using this approach, we obtained good results for 5 out of 6 machines on the development data. We also discuss the modifications needed to surpass the baseline score for the remaining (ToyConveyor) machine which we found to be particularly difficult. On the challenge evaluation test data, our results were skewed by the system’s uninspiring performance on the Toy machines. However, we placed 18th in the challenge due to our results on the industrial machine data where we reached the top 5 in team pAUC scores.