  We propose an approach for training speaker identification models in a weakly
supervised manner. We concentrate on the setting where the training data
consists of a set of audio recordings and the speaker annotation is provided
only at the recording level. The method uses speaker diarization to find unique
speakers in each recording, and i-vectors to project the speech of each speaker
to a fixed-dimensional vector. A neural network is then trained to map
i-vectors to speakers, using a special objective function that allows to
optimize the model using recording-level speaker labels. We report experiments
on two different real-world datasets. On the VoxCeleb dataset, the method
provides 94.6% accuracy on a closed set speaker identification task, surpassing
the baseline performance by a large margin. On an Estonian broadcast news
dataset, the method provides 66% time-weighted speaker identification recall at
93% precision.
