Detection of shouted speech in noise: human and machine.

High vocal effort has characteristic acoustic effects on speech. This study focuses on the utilization of this information by human listeners and a machine-based detection system in the task of detecting shouted speech in the presence of noise. Both female and male speakers read Finnish sentences using normal and shouted voice in controlled conditions, with the sound pressure level recorded. The speech material was artificially corrupted by noise and supplemented with pure noise. The human performance level was statistically evaluated by a listening test, where the subjects labeled noisy samples according to whether shouting was heard or not. A Bayesian detection system was constructed and statistically evaluated. Its performance was compared against that of human listeners, substituting different spectrum analysis methods in the feature extraction stage. Using features capable of taking into account the spectral fine structure (i.e., the fundamental frequency and its harmonics), the machine reached the detection level of humans even in the noisiest conditions. In the listening test, male listeners detected shouted speech significantly better than female listeners, especially with speakers making a smaller vocal effort increase for shouting.