Teaching athletes cognitive skills: detecting cognitive load in speech input

As part of their preparation, athletes are often required to complete cognitive skills training using targeted sports-specific software applications. When cognitive load is very high, the quality of performance can be negatively affected and learning can be inhibited. The aim of this study is to verify whether cognitive load can be inferred directly from speech signal changes collected using one such training application. We expect that the quality of the communicative signals during interaction will change as cognitive load increases. Twelve recreational basketball players completed training requiring them to recall aloud the positions of increasing numbers of team players, and draw symbols to represent those players onto a court schematic on a digital surface. This paper focuses on the analysis of the speech data only, testing whether the speech signal changes due to high cognitive load. We describe the techniques used to build the speech load models and present the classification results. Using only automated speech signal analysis, we can identify participants experiencing low or high load with an accuracy of 92.3%. We envisage it is possible to discern broad level cognitive load ranges through speech signal changes and may provide the opportunity to tailor the training application in more appropriate ways for each learner in real time.