Speaker Verification Using A Modified Adaptive GMM Approach Based On Low Rank Matrix Recovery

摘要

In this paper, we propose a new method to calculate observation confidence values that are applied in a modified adaptive GMM training for speaker verification. First, we use low rank matrix recovery (LRR) to find enhanced speeches and estimate frame SNR values. Then, a simple sigmoid function is applied to convert the frame SNR values into the observation confidence values. We also combine the frame SNR values estimated by the MMSE log-STSA and LRR methods in order to enhance performance of speaker verification system. To verify the accuracy of the system, we use utterances from a Korean movie "You came from the stars". The experimental results show that our proposed approach achieves better accuracy than the baseline GMM-UBM under both clean and noisy environments.