Speech intelligibility prediction using a Neurogram Similarity Index Measure

Discharge patterns produced by fibres from normal and impaired auditory nerves in response to speech and other complex sounds can be discriminated subjectively through visual inspection. Similarly, responses from auditory nerves where speech is presented at diminishing sound levels progressively deteriorate from those at normal listening levels. This paper presents a Neurogram Similarity Index Measure (NSIM) that automates this inspection process, and translates the response pattern differences into a bounded discrimination metric. Performance intensity functions can be used to provide additional information over measurement of speech reception threshold and maximum phoneme recognition by plotting a test subject's recognition probability over a range of sound intensities. A computational model of the auditory periphery was used to replace the human subject and develop a methodology that simulates a real listener test. The newly developed NSIM is used to evaluate the model outputs in response to Consonant-Vowel-Consonant (CVC) word lists and produce phoneme discrimination scores. The simulated results are rigorously compared to those from normal hearing subjects in both quiet and noise conditions. The accuracy of the tests and the minimum number of word lists necessary for repeatable results is established and the results are compared to predictions using the speech intelligibility index (SII). The experiments demonstrate that the proposed simulated performance intensity function (SPIF) produces results with confidence intervals within the human error bounds expected with real listener tests. This work represents an important step in validating the use of auditory nerve models to predict speech intelligibility.