In this work we propose a novel way of discriminating the words that are recognized by a speech recognition system as correctly or incorrectly detected words. The procedure consists of the extraction of a set of characteristics for each word. Utilizing these characteristics, we have built two classifiers: the first one is a vector quantizer, while the second one, though also a vector quantizer, was trained using adaptative technique learning (LVQ). The results obtained show an improvement in the performance of the recognizer achieved by reducing the number of insertions with no significant reduction in the correctly detected words.
@inproceedings{benitez98_icslp, title = {Word verification using confidence measures in speech recognition}, author = {M. Carmen Benitez and Antonio Rubio and Pedro García and Jesus Diaz-Verdejo}, year = {1998}, booktitle = {5th International Conference on Spoken Language Processing (ICSLP 1998)}, pages = {paper 1082}, doi = {10.21437/ICSLP.1998-419}, issn = {2958-1796},}Cite as:Benitez, M.C., Rubio, A., García, P., Diaz-Verdejo, J. (1998) Word verification using confidence measures in speech recognition. Proc. 5th International Conference on Spoken Language Processing (ICSLP 1998), paper 1082, doi: 10.21437/ICSLP.1998-419