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Multimodal text-audio sentiment in clinical aphasia speech using NLP
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DOI：10.3389/fdgth.2026.1740270.png)
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Abstract
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IntroductionAphasia affects expressive and receptive communication and may influence the affective tone expressed during clinical speech tasks. This study presents an exploratory weakly supervised NLP analysis of positive/negative affective-tone proxies in AphasiaBank transcripts with paired audio.MethodsWe extracted sentence embeddings from DistilBERT (e∈R768) and recording-level acoustic summaries (MFCC13; ZCR; RMS; spectral centroid; and spectral bandwidth; a∈R17). Text and acoustic features were concatenated (x=[e;a]∈R785) and classified using Random Forest models. Sentiment labels were generated using an SST-2-derived weak-supervision pipeline and should be interpreted as pseudo-labels rather than clinical ground truth. To evaluate modality contribution and potential leakage; we compared text-only; audio-only; and fused text-audio models under utterance-level and recording-disjoint splits. A small five-rater evaluation was used to examine human judgment alignment.ResultsUnder the recording-disjoint split; both the text-only and fused text-audio models achieved 97.9% accuracy and 0.791 macro-F1; while the audio-only model achieved 55.4% accuracy and 0.388 macro-F1. These results indicate that classification performance was primarily driven by textual embeddings; while the recording-level acoustic summaries did not improve performance over text-only features. Across the pseudo-labeled corpus; aphasic utterances were more often labeled negative than control utterances. Age-stratified summaries showed subgroup variation in pseudo-label distributions; but these patterns were treated descriptively because labels were model-derived. Human-rater agreement was low for aphasic utterances; indicating that affective-tone interpretation in fragmented clinical speech is ambiguous.DiscussionThese findings should be interpreted as exploratory evidence about weakly supervised affective-tone proxies; not as validated clinical sentiment recognition. The results highlight both the promise of clinical NLP for aphasia discourse analysis and the need for independent human-labeled validation; utterance-aligned acoustic features; and careful control of domain; task; age; and topic bias.
Keywords:
sentiment analysis
acoustic features
aphasia
clinical NLP
weak supervision
affective tone
DistilBERT embeddings
multimodal speech analysis
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