This paper presents a large far-field text-dependent speaker verification database named HI-MIA. We aim to meet the data requirement for far-field microphone array based speaker verification since most of the publicly available databases are single channel close-talking and text-independent. Our database contains recordings of 340 people in rooms designed for the far-field scenario. Recordings are captured by multiple microphone arrays located in different directions and distance to the speaker and a high-fidelity close-talking microphone. Besides, we propose a set of end-to-end neural network based baseline systems that adopt both single-channel and multi-channel data for training, respectively. Results show that the fusion systems could achieve 3.29\% EER in the far-field enrollment far field testing task and 4.02\% EER in the close-talking enrollment and far-field testing task.
View on arXiv