Dataset Information

Sounds of silence: Data for analysing muted safety voice in speech.

ABSTRACT: Transcribed text from simulated hazards contains important content relevant for preventing harm. By capturing and analysing the content of speech when people raise (safety voice) or withhold safety concerns (safety silence), communication patterns may be identified for when individuals perceive risk, and safety management may be improved through identifying potential antecedents. This dataset contains transcribed speech from 404 participants (n_students = 377; n_female = 277, Age M_(sd) = 22.897_(5.386)) engaged in a simulated hazardous scenario (walking across an unsafe plank), capturing 18,078 English words (M_(sd) = 46.117_(37.559)). The data was collected through the Walking the plank paradigm (Noort et al, 2019), which provides a validated laboratory experiment designed for the direct observation of communication in response to hazardous scenarios that elicit safety concerns. Three manipulations were included in the design: hazard salience (salient vs not salient), responsibilities (clear vs diffuse) and encouragements (encouraged vs discouraged). Speech between two set timepoints in the hazardous scenario was transcribed based on video recordings and coded in terms of the extent to which speech involved safety voice or safety silence. Files contain i) a .csv containing the raw data, ii) a .csv providing variable description, iii) a Jupyter notebook (v. 3.7) providing the statistical code for the accompanying research article, iv) a .html version of the Jupyter notebook, v) a .html file providing the graph for the .html Jupyter notebook, vi) speech dictionaries, and vii) a copy of the electronic questionnaire. The data and supplemental files enable future research through providing a dataset in which participants can be distinguished in terms of the extent to which they are concerned and raise or withhold this. It enables speech and conversation analyses and the Jupyter notebook may be adapted to enable the parsing and coding of text using provided, existing and custom dictionaries. This may lead to the identification of communication patterns and potential interventions for unmuting safety voice. This data-in-brief is published alongside the research article: M. C. Noort, T.W. Reader, A. Gillespie. (2021). The sounds of safety silence: Interventions and temporal patterns unmute unique safety voice content in speech. Safety Science.

SUBMITTER: Noort MC

PROVIDER: S-EPMC8182378 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:At around 7 months of age, human infants begin to reliably produce well-formed syllables containing both consonants and vowels, a behavior called canonical babbling. Over subsequent months, the frequency of canonical babbling continues to increase. How the infant's nervous system supports the acquisition of this ability is unknown. Here we present a computational model that combines a spiking neural network, reinforcement-modulated spike-timing-dependent plasticity, and a human-like vocal tract to simulate the acquisition of canonical babbling. Like human infants, the model's frequency of canonical babbling gradually increases. The model is rewarded when it produces a sound that is more auditorily salient than sounds it has previously produced. This is consistent with data from human infants indicating that contingent adult responses shape infant behavior and with data from deaf and tracheostomized infants indicating that hearing, including hearing one's own vocalizations, is critical for canonical babbling development. Reward receipt increases the level of dopamine in the neural network. The neural network contains a reservoir with recurrent connections and two motor neuron groups, one agonist and one antagonist, which control the masseter and orbicularis oris muscles, promoting or inhibiting mouth closure. The model learns to increase the number of salient, syllabic sounds it produces by adjusting the base level of muscle activation and increasing their range of activity. Our results support the possibility that through dopamine-modulated spike-timing-dependent plasticity, the motor cortex learns to harness its natural oscillations in activity in order to produce syllabic sounds. It thus suggests that learning to produce rhythmic mouth movements for speech production may be supported by general cortical learning mechanisms. The model makes several testable predictions and has implications for our understanding not only of how syllabic vocalizations develop in infancy but also for our understanding of how they may have evolved.

Dataset Information

Sounds of silence: Data for analysing muted safety voice in speech.

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