An Adaptive X-vector Model for Text-independent Speaker Verification

Abstract

In this paper, adaptive mechanisms are applied in deep neural network (DNN) training for x-vector-based text-independent speaker verification. First, adaptive convolutional neural networks (ACNNs) are employed in frame-level embedding layers, where the parameters of the convolution filters are adjusted based on the input features. Compared with conventional CNNs, ACNNs have more flexibility in capturing speaker information. Moreover, we replace conventional batch normalization (BN) with adaptive batch normalization (ABN). By dynamically generating the scaling and shifting parameters in BN, ABN adapts models to the acoustic variability arising from various factors such as channel and environmental noises. Finally, we incorporate these two methods to further improve performance. Experiments are carried out on the speaker in the wild (SITW) and VOiCES databases. The results demonstrate that the proposed methods significantly outperform the original x-vector approach.

6 pages, 3 figures

References in corpus (6)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• VoxCeleb: a large-scale speaker identification dataset
• Attentive Statistics Pooling for Deep Speaker Embedding
• Adversarial Speaker Verification
• Attentive batch normalization for lstm-based acoustic modeling of speech recognition

Cited by in corpus (1)

• Temporal Dynamic Convolutional Neural Network for Text-Independent Speaker Verification and Phonemetic Analysis