Before revision:  Third, three search methods, including manual grid search, random search, and model predictive evolutionary search, are introduced to find a trade-off between accuracy and efficiency. Results of experiments on the VoxCeleb dataset show EfficientTDNN provides a vast search space including approximately 10^{13 achieves 1.55\% EER and 0.138 DCF_{0.01 with 565M MACs as well as 0.96\% EER and 0.108 DCF_{0.01} with 1.46G MACs.
After revision:  Third, three search methods, including manual grid search, random search, and model predictive evolutionary search, are introduced to find a trade-off between accuracy and width. Secondly, the supernet is progressively trained with multi-condition data augmentation to mitigate interference between subnets and overcome the challenge of optimizing a huge search space. Thirdly, an accuracy predictor and efficiency estimator are proposed to use in the architecture search to derive the specialized subnet under the given efficiency constraints. Experimental results on the VoxCeleb dataset show EfficientTDNN provides a vast search space including approximately 10^{13 achieves 1.55\% EER and 0.138 DCF_{0.01 with 565M MACs as well as 0.96\% EER and 0.108 DCF_{0.01} with 1.46G MACs.