Large-scale learning of generalised representations for speaker recognition
This work addresses speaker recognition for varied applications, but it is incremental as it combines existing methods and data.
The paper tackled building a speaker recognition model for diverse scenarios by exploring architectures like ECAPA-TDNN and MFA-Conformer and training on large datasets up to 87k speakers and 10.22k hours of speech, resulting in an average performance improvement of over 20% across four evaluation protocols.
The objective of this work is to develop a speaker recognition model to be used in diverse scenarios. We hypothesise that two components should be adequately configured to build such a model. First, adequate architecture would be required. We explore several recent state-of-the-art models, including ECAPA-TDNN and MFA-Conformer, as well as other baselines. Second, a massive amount of data would be required. We investigate several new training data configurations combining a few existing datasets. The most extensive configuration includes over 87k speakers' 10.22k hours of speech. Four evaluation protocols are adopted to measure how the trained model performs in diverse scenarios. Through experiments, we find that MFA-Conformer with the least inductive bias generalises the best. We also show that training with proposed large data configurations gives better performance. A boost in generalisation is observed, where the average performance on four evaluation protocols improves by more than 20%. In addition, we also demonstrate that these models' performances can improve even further when increasing capacity.