Curriculum Learning Based Approaches for Noise Robust Speaker Recognition

Performance of speaker identification (SID) systems is known to degrade rapidly in the presence of mismatch such as noise and channel degradations. This study introduces a novel class of curriculum learning (CL) based algorithms for noise robust speaker recognition. We introduce CL-based approaches at two stages within a state-of-the-art speaker verification system: at the i-Vector extractor estimation and at the probabilistic linear discriminant (PLDA) back-end. Our proposed CL-based approaches operate by categorizing the available training data into progressively more challenging subsets using a suitable difficulty criterion. Next, the corresponding training algorithms are initialized with a subset that is closest to a clean noise-free set, and progressively moving to subsets that are more challenging for training as the algorithms progress. We evaluate the performance of our proposed approaches on the noisy and severely degraded data from the DARPA RATS SID task, and show consistent and significant improvement across multiple test sets over a baseline SID framework with a standard i-Vector extractor and multisession PLDA-based back-end. We also construct a very challenging evaluation set by adding noise to the NIST SRE 2010 C5 extended condition trials, where our proposed CL-based PLDA is shown to offer significant improvements over a traditional PLDA based back-end.