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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 speech and language signals. However, annotating syntactic structure for a given natural 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 on the physically overt and temporally measurable properties of the signal, while syntactic units are generally covert and their chunking 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.