Remote speaker recognition based on the enhanced LDV-captured speech

Speaker recognition technique is one of the popular biometric identification technology, which identifies the speaker's identity based on the speaker's voice. Whereas almost all speaker verification system shows poor performance when the system and speaker are far apart. To address the challenges of remote speaker recognition, a Laser Doppler Vibrometer (LDV) is used to recognize remote speaker. In this paper, three LDV speech corpuses, each consists of 50 speakers, are collected from the vibrations of a plastic bag, a mineral water bottle and a computer screen, using the LDV developed by us. The distance from the LDV sensor to the vibration targets is approximately 50 m. In order to improve the quality of the LDV-captured speech, the speech enhancement technology based on optimally modified log-spectral amplitude (OM-LSA) algorithm is used. According to the enhanced LDV-captured speech, a GMM-UBM model is built to recognize remote speaker. The experiment results show that the average EER using LDV-captured speech is 16.9590%. These results show great promise of using LDV for long range speaker recognition applications.