- Jee-weon Jung, Hee-Soo Heo, Bong-Jin Lee, Jaesong Lee, Hye-jin Shim, Youngki Kwon, Joon Son Chung, Shinji Watanabe. 2022. Large-scale learning of generalised representations for speaker recognition. Abstract: The objective of this work is to develop a speaker recognition model to be used in diverse scenarios. We hypothesise that two compo-nents 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.
