2. The method of claim 1 , wherein tuning the first-level DNN model based on the labeled speech data to produce the second-level DNN model comprises: imposing at least two constraints during adjustment of the first-level DNN based on the labeled speech data, including: (1) distances between characteristic voiceprints generated from speech samples of different speakers increase with training, and (2) distances between characteristic voice prints generated from speech samples of same speakers decrease with training.
1. A method, comprising: at a device having one or more processors and memory: establishing a first-level Deep Neural Network (DNN) model based on unlabeled speech data, the unlabeled speech data containing no speaker labels and the first-level DNN model specifying a plurality of basic voiceprint features for the unlabeled speech data; obtaining a plurality of high-level voiceprint features by tuning the first-level DNN model based on labeled speech data, the labeled speech data containing speech samples with respective speaker labels, and the tuning producing a second-level DNN model specifying the plurality of high-level voiceprint features; based on the second-level DNN model, registering a first high-level voiceprint feature sequence for a user based on a registration speech sample received from the user; and performing speaker verification for the user based on the first high-level voiceprint feature sequence registered for the user, the speaker verification comprising: receiving, from the user, a test speech sample; obtaining a second high-level voiceprint feature sequence based on the test speech sample using the first-level DNN model and the second-level DNN model in sequence; determining a distance between the second high-level voiceprint feature sequence and the first high-level voiceprint feature sequence registered for the user; and in accordance with a determination that the distance between the second high-level voiceprint feature sequence and the first high-level voiceprint feature sequence is less than a preset threshold, automatically, without user intervention, verifying the identity of the user.