DEVELOPMENT OF A MONOSYLLABIC ADAPTIVE SPEECH TEST FOR THE IDENTIFICATION OF CENTRAL AUDITORY PROCESSING DISORDER

O'Beirne  GA, McGaffin AJ.

University of Canterbury, New Zealand.

One category of tests that examine auditory processing disorders (APD) are the various versions of the "filtered words test" (FWT), whereby a monaural, low-redundancy speech sample is distorted by using filtering to modify its frequency content.

Due to the richness of the neural pathways in the auditory system and the redundancy of acoustic information in spoken language, a normal listener is able to recognize speech even when parts of the signal are missing, whereas this ability is often impaired in listeners with APD. One limitation of the various versions of the FWT is that they are carried out using a constant level of low-pass filtering (e.g. a fixed 1 kHz corner frequency) which makes them prone to ceiling and floor effects. The purpose of this study was to counter these effects by modifying the FWT to use a computer-based adaptive procedure, to improve the sensitivity of the test over its constant-level counterparts.

In this preliminary study, 23 normal adults and 32 normal children (aged 7 to 11 years) were tested using the University of Canterbury Monosyllabic Adaptive Speech Test (UC MAST), a four-alternative forced-choice adaptive test that altered the low-pass filter (LPF) level to track the corner frequency at which participants correctly identified a certain percentage of the words.

Findings indicated a significant maturational effect on the UC MAST results. Adult participants performed significantly better on the UC MAST in comparison to the child participants (50% correct scores at 419 ± 108 Hz LPF for adults compared to 688 ± 193 Hz LPF in children). The UC MAST test was reliable over repeated administrations. The development of the UC MAST is discussed and the clinical implications of the findings are explored. Further testing using a population of participants with auditory processing disorders is planned.

E-mail: gregory.obeirne@canterbury.ac.nz