This study investigates how domain expertise shapes spontaneous oral language production, with a focus on architecture. Building on the ExpLay Corpus, which contains image descriptions by speakers with and without architectural training, we analyze linguistic variation by combining Profiling-UD and the DECAF framework. We extract a broad range of syntactic and morpho-syntactic features to build linguistic profiles for both groups and train classifiers to distinguish expert from non-expert productions. Two traditional machine learning models (logistic regression and SVM) are compared with a lightweight BiLSTM and two large language models (GliClass and LLaMA 2). While the expert and non-expert corpora diverge only subtly (pairwise Jensen–Shannon divergence (JSD)= 0.25), the BiLSTM using fastText embeddings achieves the highest F1-score (0.88), outperforming both traditional models and LLMs. This indicates that semantic representations are more predictive of domain variation than purely structural features and that smaller neural architectures generalize better on limited data. Overall, the findings provide empirical evidence that architectural expertise leaves measurable linguistic traces in spontaneous speech, supporting the Grammar of Space hypothesis.

This paper introduces the ExpLay-Pipeline, a novel semi-automated processing tool designed for the analysis of language production data from experts in comparison to the language production of a control group of laypeople. The pipeline combines manual annotation and curation with state-of-the-art machine learning and rule-based methods, following a silver standard approach. It integrates various analysis modules specifically for the syntactic and lexical evaluation of parsed linguistic data. While implemented initially for the creation of the ExpLay-Corpus, it is designed for the processing of linguistic data in general. The paper details the design and implementation of this pipeline.