Sensing of inspiration events from speech: comparison of deep learning and linguistic methods
This study provides incremental insights for developing VRB methods and understanding speech breathing behavior, benefiting researchers in respiratory health monitoring and speech processing.
The paper tackled the problem of detecting inspiration events from speech by comparing a novel neural Virtual Respiratory Belt (VRB) algorithm with linguistic methods, finding that the VRB method outperformed word pause detection and grammatical content segmentation, with results showing significant ungrammatical breathing in both read and spontaneous speech.
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.