End-to-end Training Approaches for Discriminative Segmental Models
Table 8: Forced alignment quality on the test set as a percentage of correctly positioned phone boundaries within a predefined tolerance, measured with the best-performing segmental model trained with marginal log loss.
['[ITALIC] t≤10ms', '[ITALIC] t≤20ms', '[ITALIC] t≤30ms', '[ITALIC] t≤40ms']
Next, since the system trained with marginal log loss does not use the ground truth segmentations, and since the evaluation measure (phone error rate) does not consider segmentations, we do not know if the system is able to discover reasonble phone boundaries without supervision. We approach this question by aligning the label sequences to the acoustics, and compare the resulting segmentations against the manually annotated segmentations. Limiting the maximum duration to 30 frames also affects the alignment performance.