Error correction of proportions in spoken opinion surveys

The paper analyzes the types of errors encountered in automatic spoken surveys. These errors are different from the ones that appear when surveys are taken by humans because they are caused by the imprecision of an automatic system. Previous studies presented a strategy that consists in the robust detection of subjective opinions about a particular topic in a spoken message. If the same automatic system is used for estimating opinion proportions in different spoken surveys, then the error rate of the entire automatic process should not vary too much in different surveys for each type of opinions. Based on this conjecture, a linear error model is derived and used for error correction. Experimental results obtained with data of a real-world deployed system show significant error reductions obtained in the automatic estimation of proportions in spoken surveys.