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.

本研究旨在开发一种演讲者识别模型，该模型需要经过充分的架构和大量数据的训练，最终能够在不同的场景下得到实际应用，并且经过实验证明，采用 MFA-Conformer 模型和大数据训练配置可以使性能提高超过 20%。