The 56% agreement rate between the TEN and PTC tasks indicates that at least one of these tasks was only partially reliable as a diagnostic tool. Factors unrelated to the presence of dead regions may contribute to excess masking in TEN without producing tip shifts in PTCs. Thus it may be appropriate to view tuning curve results as more reliable in cases where TEN and PTC results disagree. The current results do not provide support for the TEN task as a reliable diagnostic tool for identification of dead regions.

This article introduces a new model that predicts speech intelligibility based on statistical decision theory. This model, which we call the speech recognition sensitivity (SRS) model, aims to predict speech-recognition performance from the long-term average speech spectrum, the masking excitation in the listener's ear, the linguistic entropy of the speech material, and the number of response alternatives available to the listener. A major difference between the SRS model and other models with similar aims, such as the articulation index, is this model's ability to account for synergetic and redundant interactions among spectral bands of speech. In the SRS model, linguistic entropy affects intelligibility by modifying the listener's identification sensitivity to the speech. The effect of the number of response alternatives on the test score is a direct consequence of the model structure. The SRS model also appears to predict the differential effect of linguistic entropy on filter condition and the interaction between linguistic entropy, signal-to-noise ratio, and language proficiency.

Absolute thresholds for and loudness matches between pure tones and four- and ten-tone complexes were used to assess the form of the function relating loudness to sensation level, SL, at low and moderate levels. The components of the tone complexes had equal SLs and were separated by one, two, four, or six critical bands. Six listeners with normal hearing were tested. The thresholds for the multitone complexes indicate that they generally can be detected even when the level of a single component is a few dB below the threshold. The average detection advantage is consistent with predictions for multiple observations in independent, frequency-selective auditory channels, but differences among listeners are apparent. The loudness matches also vary somewhat among listeners. Five of the six listeners matched tone complexes composed of subthreshold components to a pure tone a few dB above threshold. This indicates that the loudness of tones at or even below threshold is greater than zero for these five listeners. A simple model of loudness summation was used to obtain loudness functions from the individual listeners' loudness matches. The slopes of the loudness functions [log(loudness) plotted as a function of log(intensity)] generally exceed unity at low levels and are near 0.2 at 40 dB SL. This shallow slope at moderate levels agrees with loudness functions derived from data on temporal integration of loudness. The average loudness function derived from the present data also is in good agreement with a variety of previous data obtained by magnitude estimation, magnitude production, ratio production, and measurements of binaural loudness summation.