A highly localised data-association measure, termed intrinsic synchrosqueezing transform (ISC), is proposed for the analysis of coupled nonlinear and nonstationary multivariate signals. This is achieved based on a combination of noise-assisted multivariate empirical mode decomposition (NA-MEMD) and short-time Fourier transform (STFT)-based univariate and multivariate synchrosqueezing transforms (FSST and F-MSST). It is shown that the ISC outperforms six other combinations of algorithms in estimating degrees of synchrony in synthetic linear and nonlinear bivariate signals. Its advantage is further illustrated in the precise identification of the synchronised respiratory and HRV frequencies among a subset of bass singers of a professional choir, where it distinctly exhibits better performance than the continuous wavelet transform (CWT)-based ISC. We also introduce an extension to intrinsic phase synchrony (IPS) measure, referred to as nested intrinsic phase synchrony (N-IPS), for the empirical quantification of physically meaningful and straightforward to interpret trends in phase synchrony. The N-IPS is employed to reveal physically meaningful variations in the levels of cooperation in choir singing and performing a surgical procedure. Both the proposed techniques successfully reveal degrees of synchronisation of the physiological signals in two different aspects: (i) precise localisation of synchrony in time and frequency (ISC), and (ii) large scale analysis for the empirical quantification of physically meaningful trends in synchrony (N-IPS).