The development and evaluation of a new speech-intelligibility test suitable for routine use by operational personnel in determining the performance level of speech-communication systems is described. The format used is similar to that described for a rhyme test but makes use of a closed-response set. An experiment was performed to determine the general reliability of the test materials when administered to U.S. Air Force enlisted personnel under a wide range of signal-to-noise ratios. Testing of 18 listeners over a period of 30 days showed that repeated exposure to the materials did not change the levels of average response in any appreciable way. Analysis of the responses to individual phonetic elements shows that the test can be useful for diagnostic study as well as for over-all evaluation of communication systems. Talker differences that appeared during the experiment and the statistical reliability and sensitivity of the materials are analyzed and discussed.

Speech-intelligibility testing is an expensive and time-consuming operation that requires laboratory test conditions. In an attempt to short-cut or make unnecessary this type of testing, a procedure was developed by French and Steinberg [J. Acoust. Soc. Am. 19, 90–119 (1949)] for calculating from physical and acoustical measurements made on a communication system a measure that is indicative of the intelligibility scores that would be obtained for that system under actual test conditions. This measure is called the “Articulation Index” (AI). Methods of calculating AI have been improved and elaborated to the point where several methods for its calculation, herein reported, can be proposed for use in the evaluation of most speech communication systems.

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French and Steinberg proposed the basic concept and method of calculating the Articulation Index (AI) approximately 15 years ago. Although improvements and modifications of AI have been made over the years, it has not been generally accepted, perhaps because of insufficient evidence as to its validity. On the basis of studies reported in the literature and on new studies herein reported, it is shown that AI is a valid predictor of the intelligibility of speech under a wide variety of conditions of noise masking and speech distortion.

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