Deep Speaker Feature Learning for Text-independent Speaker Verification

Understand

Recently deep neural networks (DNNs) have been used to learn speaker features.

• However, the quality of the learned features is not sufficiently good, so a complex back-end model, either neural or probabilistic, has to be used to address the residual uncertainty when applied to speaker verification, just as with raw features.
• This paper presents a convolutional time-delay deep neural network structure (CT-DNN) for speaker feature learning.
• Our experimental results on the Fisher database demonstrated that this CT-DNN can produce high-quality speaker features: even with a single feature (0.3 seconds including the context), the EER can be as low as 7.68%.