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Respiratory chest belt sensor can be used to measure the respiratory rate andother respiratory health parameters. Virtual Respiratory Belt, VRB, algorithmsestimate the belt sensor waveform from speech audio. In this paper we comparethe detection of inspiration events (IE) from respiratory belt sensor datausing a novel neural VRB algorithm and the detections based on time-alignedlinguistic content. The results show the superiority of the VRB method overword pause detection or grammatical content segmentation. The comparison of themethods show that both read and spontaneous speech content has a significantamount of ungrammatical breathing, that is, breathing events that are notaligned with grammatically appropriate places in language. This study gives newinsights into the development of VRB methods and adds to the generalunderstanding of speech breathing behavior. Moreover, a new VRB method, VRBOLA,for the reconstruction of the continuous breathing waveform is demonstrated.

Sensing of inspiration events from speech: comparison of deep learning and linguistic methods