Humans do not Maximize the Probability of Correct Decision When Recognizing DANTALE Words in Noise

Inspired by the DANTALE II listening test paradigm, which is used for determining the intelligibility of noisy speech, we assess the hypothesis that humans maximize the probability of correct decision when recognizing words contaminated by additive Gaussian, speech-shaped noise. We first propose a statistical Gaussian communication and classification scenario, where word models are built from short term spectra of human speech, and optimal classifiers in the sense of maximum a posteriori estimation are derived. Then, we perform a listening test, where the participants are instructed to make their best guess of words contaminated with speech-shaped Gaussian noise. Comparing the human’s performance to that of the optimal classifier reveals that at high SNR, humans perform comparable to the optimal classifier. However, at low SNR, the human performance is inferior to that of the optimal classifier. This shows that, at least in this specialized task, humans are generally not able to maximize the probability of correct decision, when recognizing words.