  We develop two complementary advances for training no-reference (NR) speech
quality estimators with independent datasets. Multi-dataset finetuning (MDF)
pretrains an NR estimator on a single dataset and then finetunes it on multiple
datasets at once, including the dataset used for pretraining. AlignNet uses an
AudioNet to generate intermediate score estimates before using the Aligner to
map intermediate estimates to the appropriate score range. AlignNet is agnostic
to the choice of AudioNet so any successful NR speech quality estimator can
benefit from its Aligner. The methods can be used in tandem, and we use two
studies to show that they improve on current solutions: one study uses nine
smaller datasets and the other uses four larger datasets. AlignNet with MDF
improves on other solutions because it efficiently and effectively removes
misalignments that impair the learning process, and thus enables successful
training with larger amounts of more diverse data.
