The test sentences are not used in any portion of the training process. For training, the sentences are divided automatically in five equal portions to provide an initial segmention into component signs. Then, initial estimates for the means and variances of the output probabilities are provided by iteratively using Viterbi alignment on the training data and then recomputing the means and variances by pooling the vectors in each segment. Contexts are not used since they would require significantly more data to train. However, a similar effect can be achieved with the strong grammar in this data set.
Recognition occurs five times faster than real time.
Accuracy is calculated by where N is the total number of words in the test set, D is the number of deletions, S is the number of substitutions, and I is the number of insertions. Without the grammar, the recognizer is allowed to match the observation vectors with any number of the 40 vocabulary words in any order. The absolute number of errors of each type are listed in Table 2 . Many of the insertion errors correspond to signs with repetitive motion. This provides a sense of how the recognizer performs when only relative features are available.