  The performance of speaker-related systems usually degrades heavily in
practical applications largely due to the presence of background noise. To
improve the robustness of such systems in unknown noisy environments, this
paper proposes a simple pre-processing method called Noise Invariant Frame
Selection (NIFS). Based on several noisy constraints, it selects noise
invariant frames from utterances to represent speakers. Experiments conducted
on the TIMIT database showed that the NIFS can significantly improve the
performance of Vector Quantization (VQ), Gaussian Mixture Model-Universal
Background Model (GMM-UBM) and i-vector-based speaker verification systems in
different unknown noisy environments with different SNRs, in comparison to
their baselines. Meanwhile, the proposed NIFS-based speaker verification
systems achieves similar performance when we change the constraints
(hyper-parameters) or features, which indicates that it is robust and easy to
reproduce. Since NIFS is designed as a general algorithm, it could be further
applied to other similar tasks.
