  Respiratory chest belt sensor can be used to measure the respiratory rate and
other respiratory health parameters. Virtual Respiratory Belt, VRB, algorithms
estimate the belt sensor waveform from speech audio. In this paper we compare
the detection of inspiration events (IE) from respiratory belt sensor data
using a novel neural VRB algorithm and the detections based on time-aligned
linguistic content. The results show the superiority of the VRB method over
word pause detection or grammatical content segmentation. The comparison of the
methods show that both read and spontaneous speech content has a significant
amount of ungrammatical breathing, that is, breathing events that are not
aligned with grammatically appropriate places in language. This study gives new
insights into the development of VRB methods and adds to the general
understanding of speech breathing behavior. Moreover, a new VRB method, VRBOLA,
for the reconstruction of the continuous breathing waveform is demonstrated.
