Study of human and machine discrete utterance recognition (DUR)
- 24 March 2005
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 7, 2022-2025
- https://doi.org/10.1109/icassp.1982.1171847
Abstract
Performance evaluation of DUR systems has typically consisted of percentage-correct recognition (PCR) for specific vocabularies. This numerical measure is misleading because it presumes that 100% recognition is equally achievable for all vocabularies. This, in fact, is not the case. In this paper, results from an experiment which compared human-listener performance to that of a particular recognition machine will be presented. Three different vocabularies were studied. Preliminary results for normalizing machine performance with respect to the difficulty of a test vocabulary are given. Relevant data from the experiment are included to demonstrate the problem and its potential solution.Keywords
This publication has 3 references indexed in Scilit:
- What are the significant variables in dynamic programming for discrete utterance recognition?Published by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- State constrained dynamic programming (SCDP) for discrete utterance recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- A Minimum-Distance Search Technique and its Application to Automatic Directory AssistanceBell System Technical Journal, 1980