Voice and voice states can alter the acoustic properties of speech, which can affect the performance of paralysis models for the influence of people with atypical speech. Evaluate published models for recognizing the impact of categories and dimensions from speech on atypical speech datasets and compare them with typical speech datasets. We investigate three dimensions of atypicality of speech. This is related to the declaration. Monofitches, which are associated with prosodicity and harshness, are related to speech quality. We look at (1) the distribution trends of categorical influence predictions within the dataset, (2) the distribution comparison of categorical influence predictions with similar datasets of typical speech, and (3) the correlation intensity between text and speech predictions of valence and arousal spontaneous speech. It can be seen that the output of the impact model is greatly influenced by the presence and extent of speech atypicals. For example, the predicted percentage of speech as SAD is significantly higher for all types and atypical speech grades when compared to similar typical speech data sets. A preliminary study on the improvement of atypical speech robustness reveals that fine-tuning models of pseudo-labeled atypical speech data improve atypical speech performance without affecting typical speech performance. Our results highlight the need for a broader training and evaluation dataset for speech emotion models, as well as the need for modeling a robust approach to speech and speech differences.
