American Sign Language fingerspelling recognition from video: Methods for unrestricted recognition and signer-independence
Table 3.4: Letter error rates (%) with iterated forced-alignment (FA) adaptation.
['Fine-tuning with FA Signer 1', 'Fine-tuning with FA Signer 2', 'Fine-tuning with FA Signer 3', 'Fine-tuning with FA Signer 4', 'Fine-tuning with FA  [BOLD] Mean', 'Fine-tuning with FA + realignment Signer 1', 'Fine-tuning with FA + realignment Signer 2', 'Fine-tuning with FA + realignment Signer 3', 'Fine-tuning with FA + realignment Signer 4', 'Fine-tuning with FA + realignment  [BOLD] Mean']
We next attempt to improve the performance of adaptation in the absence of ground-truth (manually annotated) peak labels. Using only the letter label sequence for the adaptation data, we use the signer-independent tandem recognizer to get force-aligned frame labels. We then adapt (fine-tune) the DNNs using the force-aligned adaptation data (as before in the FA case). We then re-align the test signer’s adaptation data with the adapted recognizer. Finally, we adapt the DNNs again with the re-aligned data. Throughout this experiment, we do not change the recognizer but only update the DNNs. We use first-pass SCRF with letter classifiers for these experiments.