|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
AUTOMATIC CAPITALISATION GENERATION FOR SPEECH INPUT (PREPRINT VERSION)
Ji-Hwan Kim and P.C. Woodland
Two different systems are proposed for the task of capitalisation generation. The first system is a slightly modified speech recogniser. In this system, every word in the vocabulary is duplicated: once in a decapitalised form and again in capitalised forms. In addition, the language model is re-trained on mixed case texts. The other system is based on Named Entity (NE) recognition and punctuation generation, since most capitalised words are the first words in sentences or NE words. Both systems are compared when every procedure is fully automated. The system based on NE recognition and punctuation generation shows better results by word error rate, by F-measure and by slot error rate than the system modified from the speech recogniser. This is because the latter system has a distorted language model and a sparser language model. The detailed performance of the system based on NE recognition and punctuation generation is investigated by including one or more of the following: the reference word sequences, the reference NE classes and the reference punctuation marks. The results show that this system is robust to NE recognition errors. Although most punctuation generation errors cause errors in this capitalisation generation system, the number of errors caused in capitalisation generation does not exceed the number of errors from punctuation generation. In addition, the results demonstrate that the effect of NE recognition errors is independent of the effect of punctuation generation errors for capitalisation generation.
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