  Lipreading is a difficult gesture classification task. One problem in
computer lipreading is speaker-independence. Speaker-independence means to
achieve the same accuracy on test speakers not included in the training set as
speakers within the training set. Current literature is limited on
speaker-independent lipreading, the few independent test speaker accuracy
scores are usually aggregated within dependent test speaker accuracies for an
averaged performance. This leads to unclear independent results. Here we
undertake a systematic survey of experiments with the TCD-TIMIT dataset using
both conventional approaches and deep learning methods to provide a series of
wholly speaker-independent benchmarks and show that the best
speaker-independent machine scores 69.58% accuracy with CNN features and an SVM
classifier. This is less than state of the art speaker-dependent lipreading
machines, but greater than previously reported in independence experiments.
