Abstract To investigate how the human brain encodes the complex dynamics of natural languages, any viable and reproducible analysis pipeline must rely on either manual annotations or Natural Language Processing (NLP) tools, which extract relevant physical (e.g., acoustic, gestural), and structure- building information from both speech and language signals. However, correctly annotating syntactic structure for a given human language is arguably a harder task than annotating the onset and offset of speech units such as phonemes and syllables, as the latter can be identified by relying at least in part on the physically overt and temporally measurable properties of the signal, while syntactic structure units are generally covert and their labelling is model-driven. We describe and validate a pipeline that takes into account both physical and theoretical aspects of speech and language signals, and operates a theory-driven and explicit alignment between overt speech units and covert syntactic units.