The fixed time to produce phone labels, which must be performed before translation, becomes a greater proportion of overall training time at lower-resource settings.
the phone end-to-end model offers similar training time reduction over the baseline to , where downsampling reduces sequence lengths by up to 60%, with unreduced sequence lengths through earlier convergence; this model offers a better trade-off between time and performance.