10.3389/fpsyg.2012.00132

Vollständiger Abstract

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<h4>Introduction</h4>Aphasia 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.<h4>Methods</h4>We 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.<h4>Results</h4>Under 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.<h4>Discussion</h4>These 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.

Abstract: PubMed · Datensatz