Title: End-to-End Attention based Text-Dependent Speaker Verification,
Abstract: A new type of End-to-End system for text-dependent speaker verification is
presented in this paper. Previously, using the phonetically
discriminative/speaker discriminative DNNs as feature extractors for speaker
verification has shown promising results. The extracted frame-level (DNN
bottleneck, posterior or d-vector) features are equally weighted and aggregated
to compute an utterance-level speaker representation (d-vector or i-vector). In
this work we use speaker discriminative CNNs to extract the noise-robust
frame-level features. These features are smartly combined to form an
utterance-level speaker vector through an attention mechanism. The proposed
attention model takes the speaker discriminative information and the phonetic
information to learn the weights. The whole system, including the CNN and
attention model, is joint optimized using an end-to-end criterion. The training
algorithm imitates exactly the evaluation process --- directly mapping a test
utterance and a few target speaker utterances into a single verification score.
The algorithm can automatically select the most similar impostor for each
target speaker to train the network. We demonstrated the effectiveness of the
proposed end-to-end system on Windows $10$ "Hey Cortana" speaker verification
task.