Review of methods for conducting speech research with minimally verbal individuals with autism spectrum disorder

The purpose of this paper was to review best-practice methods of collecting and analyzing speech production data from minimally verbal autistic speakers. Data on speech production data in minimally verbal individuals are valuable for a variety of purposes, including phenotyping, clinical assessment, and treatment monitoring. Both perceptual (by ear) and acoustic analyses of speech can reveal subtle improvements as a result of therapy that may not be apparent when correct/incorrect judgments are used. Key considerations for collecting and analyzing speech production data from this population are reviewed. The definition of minimally verbal that is chosen will vary depending on the specific hypotheses investigated, as will the stimuli to be collected and the task(s) used to elicit them. Perceptual judgments are ecologically valid but subject to known sources of bias; therefore, training and reliability procedures for perceptual analyses are addressed, including guidelines on how to select vocalizations for inclusion or exclusion. Factors to consider when recording and acoustically analyzing speech are also briefly discussed. In summary, the tasks, stimuli, training methods, analysis type(s), and level of detail that yield the most reliable data to answer the question should be selected. It is possible to obtain rich high-quality data even from speakers with very little speech output. This information is useful not only for research but also for clinical decision-making and progress monitoring.

Automated summary

This paper aims to review the best-practice methods of collecting and analyzing speech production data from minimally verbal autistic speakers. Speech production data in this population are valuable for various purposes, including phenotyping, clinical assessment, and treatment monitoring. Both perceptual and acoustic analyses of speech can reveal subtle improvements due to therapy. Several key considerations for data collection and analysis are discussed.