Speech and voice situations can alter the acoustic properties of speech, which may impression the efficiency of paralinguistic fashions for have an effect on for folks with atypical speech. We consider publicly accessible fashions for recognizing categorical and dimensional have an effect on from speech on a dataset of atypical speech, evaluating outcomes to datasets of typical speech. We examine three dimensions of speech atypicality: intelligibility, which is expounded to pronounciation; monopitch, which is expounded to prosody, and harshness, which is expounded to voice high quality. We take a look at (1) distributional traits of categorical have an effect on predictions inside the dataset, (2) distributional comparisons of categorical have an effect on predictions to comparable datasets of typical speech, and (3) correlation strengths between textual content and speech predictions for spontaneous speech for valence and arousal. We discover that the output of have an effect on fashions is considerably impacted by the presence and diploma of speech atypicalities. As an example, the proportion of speech predicted as unhappy is considerably increased for every type and grades of atypical speech when in comparison with comparable typical speech datasets. In a preliminary investigation on enhancing robustness for atypical speech, we discover that fine-tuning fashions on pseudo-labeled atypical speech knowledge improves efficiency on atypical speech with out impacting efficiency on typical speech. Our outcomes emphasize the necessity for broader coaching and analysis datasets for speech emotion fashions, and for modeling approaches which can be strong to voice and speech variations.
