Abstract

It was investigated whether the model for context effects, developed earlier by Bronkhorst et al. [J. Acoust. Soc. Am. 93, 499-509 (1993)], can be applied to results of sentence tests, used for the evaluation of speech recognition. Data for two German sentence tests, that differed with respect to their semantic content, were analyzed. They had been obtained from normal-hearing listeners using adaptive paradigms in which the signal-to-noise ratio was varied. It appeared that the model can accurately reproduce the complete pattern of scores as a function of signal-to-noise ratio: both sentence recognition scores and proportions of incomplete responses. In addition, it is shown that the model can provide a better account of the relationship between average word recognition probability (p(e)) and sentence recognition probability (p(w)) than the relationship p(w) =p(e)j, which has been used in previous studies. Analysis of the relationship between j and the model parameters shows that j is, nevertheless, a very useful parameter, especially when it is combined with the parameter j', which can be derived using the equivalent relationship p(w,0) = (1 - p(e))(j'), where p(w,0) is the probability of recognizing none of the words in the sentence. These parameters not only provide complementary information on context effects present in the speech material, but they also can be used to estimate the model parameters. Because the model can be applied to both speech and printed text, an experiment was conducted in which part of the sentences was presented orthographically with 1-3 missing words. The results revealed a large difference between the values of the model parameters for the two presentation modes. This is probably due to the fact that, with speech, subjects can reduce the number of alternatives for a certain word using partial information that they have perceived (i.e., not only using the sentence context). A method for mapping model parameters from one mode to the other is suggested, but the validity of this approach has to be confirmed with additional data.

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