  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 language model -- which tends to correct non-standard sequences
of words -- with the lack of language model 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
language model. The best fully automatic system achieves up to 76.06 % of
accuracy (without language model), significantly higher, 3 % above, than a
system employing word transcriptions decoded using general purpose language
models.
