  The conventional speaker recognition frameworks (e.g., the i-vector and
CNN-based approach) have been successfully applied to various tasks when the
channel of the enrolment dataset is similar to that of the test dataset.
However, in real-world applications, mismatch always exists between these two
datasets, which may severely deteriorate the recognition performance.
Previously, a few channel compensation algorithms have been proposed, such as
Linear Discriminant Analysis (LDA) and Probabilistic LDA. However, these
methods always require the collections of different channels from a specific
speaker, which is unrealistic to be satisfied in real scenarios. Inspired by
domain adaptation, we propose a novel deep-learning based speaker recognition
framework to learn the channel-invariant and speaker-discriminative speech
representations via channel adversarial training. Specifically, we first employ
a gradient reversal layer to remove variations across different channels. Then,
the compressed information is projected into the same subspace by adversarial
training. Experiments on test datasets with 54,133 speakers demonstrate that
the proposed method is not only effective at alleviating the channel mismatch
problem, but also outperforms state-of-the-art speaker recognition methods.
Compared with the i-vector-based method and the CNN-based method, our proposed
method achieves significant relative improvement of 44.7% and 22.6%
respectively in terms of the Top1 recall.
