  Speaker recognition is the process of identifying a speaker based on the
voice. The technology has attracted more attention with the recent increase in
popularity of smart voice assistants, such as Amazon Alexa. In the past few
years, various convolutional neural network (CNN) based speaker recognition
algorithms have been proposed and achieved satisfactory performance. However,
convolutional operations are building blocks that typically perform on a local
neighborhood at a time and thus miss to capture global, long-range interactions
at the feature level which are critical for understanding the pattern in a
speaker's voice. In this work, we propose to apply Non-local Convolutional
Neural Networks (NLCNN) to improve the capability of capturing long-range
dependencies at the feature level, therefore improving speaker recognition
performance. Specifically, we introduce non-local blocks where the output
response of a position is computed as a weighted sum of the input features at
all positions. Combining non-local blocks with pre-defined CNN networks, we
investigate the effectiveness of NLCNN models. Without extensive tuning, the
proposed NLCNN models outperform state-of-the-art speaker recognition
algorithms on the public Voxceleb dataset. What's more, we investigate
different types of non-local operations applied to the frequency-time domain,
time domain, frequency domain and frame-level respectively. Among them, time
domain is the most effective one for speaker recognition applications.
