Abstract
Linguistic laws constitute one of the quantitative cornerstones of modern cognitive sciences and have been routinely investigated in written corpora, or in the equivalent transcription of oral corpora. This means that inferences of statistical patterns of language in acoustics are biased by the arbitrary, language-dependent segmentation of the signal, and virtually precludes the possibility of making comparative studies between human voice and other animal communication systems. Here we bridge this gap by proposing a method that allows to measure such patterns in acoustic signals of arbitrary origin, without needs to have access to the language corpus underneath. The method has been applied to sixteen different human languages, recovering successfully some well-known laws of human communication at timescales even below the phoneme and finding yet another link between complexity and criticality in a biological system. These methods further pave the way for new comparative studies in animal communication or the analysis of signals of unknown code.
Highlights
Acoustic communication is fully determined by three physical magnitudes extracted from the signals: frequency, energy and time[35,36]
This has been shown to be the case for human voice and we can express the collapsed energy distribution as P(E) =E−φ (E/Eξ) for E El, where El is the lower limit beyond which this law is fulfilled, is a scaling function and the relevant variable is φ, the scaling exponent
In what follows we explore the emergence of classical linguistic laws in these acoustic signals
Summary
Acoustic communication is fully determined by three physical magnitudes extracted from the signals: frequency, energy and time[35,36]. We will show clear evidence of robust Zipf, Heaps and brevity laws emerging in this context and speculate that this might be due to the fact that human voice seems to be operating close to a critical state, finding an example of a biological system that, driven by evolution, has linked complexity and criticality We expect that this methodology can open a fresh line of research in communication systems where a direct exploration of underlying statistical patterns in acoustic signals is possible without needs to predetermine any of the aforementioned non-physical concepts, and hope that this will allow researchers to develop comparative studies between human language and other acoustical communication systems or even to unravel whether if a generic signal shares these patterns[2]
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