  This work aims to automatically evaluate whether the language development of
children is age-appropriate. Validated speech and language tests are used for
this purpose to test the auditory memory. In this work, the task is to
determine whether spoken nonwords have been uttered correctly. We compare
different approaches that are motivated to model specific language structures:
Low-level features (FFT), speaker embeddings (ECAPA-TDNN), grapheme-motivated
embeddings (wav2vec 2.0), and phonetic embeddings in form of senones (ASR
acoustic model). Each of the approaches provides input for VGG-like 5-layer CNN
classifiers. We also examine the adaptation per nonword. The evaluation of the
proposed systems was performed using recordings from different kindergartens of
spoken nonwords. ECAPA-TDNN and low-level FFT features do not explicitly model
phonetic information; wav2vec2.0 is trained on grapheme labels, our ASR
acoustic model features contain (sub-)phonetic information. We found that the
more granular the phonetic modeling is, the higher are the achieved recognition
rates. The best system trained on ASR acoustic model features with VTLN
achieved an accuracy of 89.4% and an area under the ROC (Receiver Operating
Characteristic) curve (AUC) of 0.923. This corresponds to an improvement in
accuracy of 20.2% and AUC of 0.309 relative compared to the FFT-baseline.
