A novel feature sub-sampling method for efficient universal background model training in speaker verification

Speaker recognition/verification systems require an extensive universal background model (UBM), which typically requires extensive resources, especially if new channel domains are considered. In this study we propose an effective and computationally efficient algorithm for training the UBM for speaker verification. A novel method based on Euclidean distance between features is developed for effective sub-sampling of potential training feature vectors. Using only about 1.5 seconds of data from each development utterance, the proposed UBM training method drastically reduces the computation time, while improving, or at least retaining original speaker verification system performance. While methods such as factor analysis can mitigate some of the issues associated with channel/microphone/environmental mismatch, the proposed rapid UBM training scheme offers a viable alternative for rapid environment dependent UBMs.