Influence of ASR and Language Model on Alzheimer's Disease Detection

Abstract: Alzheimer's Disease is the most common form of dementia. Automatic detection from speech could help to identify symptoms at early stages, so that preventive actions can be carried out. This research is a contribution to the ADReSSo Challenge, we analyze the usage of a SotA ASR system to transcribe participant's spoken descriptions from a picture. We analyse the loss of performance regarding the use of human transcriptions (measured using transcriptions from the 2020 ADReSS Challenge). Furthermore, we study the influence of a LLM -- which tends to correct non-standard sequences of words -- with the lack of LLM to decode the hypothesis from the ASR. This aims at studying the language bias and get more meaningful transcriptions based only on the acoustic information from patients. The proposed system combines acoustic -- based on prosody and voice quality -- and lexical features based on the first occurrence of the most common words. The reported results show the effect of using automatic transcripts with or without LLM. The best fully automatic system achieves up to 76.06 % of accuracy (without LLM), significantly higher, 3 % above, than a system employing word transcriptions decoded using general purpose LLMs.