  Learning an effective speaker representation is crucial for achieving
reliable performance in speaker verification tasks. Speech signals are
high-dimensional, long, and variable-length sequences containing diverse
information at each time-frequency (TF) location. The standard convolutional
layer that operates on neighboring local regions often fails to capture the
complex TF global information. Our motivation is to alleviate these challenges
by increasing the modeling capacity, emphasizing significant information, and
suppressing possible redundancies. We aim to design a more robust and efficient
speaker recognition system by incorporating the benefits of attention
mechanisms and Discrete Cosine Transform (DCT) based signal processing
techniques, to effectively represent the global information in speech signals.
To achieve this, we propose a general global time-frequency context modeling
block for speaker modeling. First, an attention-based context model is
introduced to capture the long-range and non-local relationship across
different time-frequency locations. Second, a 2D-DCT based context model is
proposed to improve model efficiency and examine the benefits of signal
modeling. A multi-DCT attention mechanism is presented to improve modeling
power with alternate DCT base forms. Finally, the global context information is
used to recalibrate salient time-frequency locations by computing the
similarity between the global context and local features. This effectively
improves the speaker verification performance compared to the standard ResNet
model and Squeeze & Excitation block by a large margin. Our experimental
results show that the proposed global context modeling method can efficiently
improve the learned speaker representations by achieving channel-wise and
time-frequency feature recalibration.
