![]() ![]() ![]() #> utteranceId utteranceStartSeconds utteranceStartTime utteranceEndSeconds # Three records from the sample transcript dataset Utterances are marked as a function of pauses in speech and/or speaker changes. Each row represents a single marked “utterance” using Zoom’s cloud-based transcription algorithm. transcript is a ame that is the parsed audio transcript file. Within this list you will find a transcript dataset. If you have followed the steps outlined in Part 2, you will have a single list object from your batch analysis. Whether a given aggregation is appropriate for assessing some construct, however, will fundamentally depend on the phenomenon under investigation. zoomGroupStats provides basic functionality to derive these kinds of aggregated metrics. For many questions, researchers are not interested in the fine-grained events rather, researchers are interested in using those fine-grained events to measure attributes of individuals or groups within certain segments of time. Depending on the rate of speech, this could present 50,000 to 60,000 spoken utterances.īecause virtual meetings present data on events in time (e.g., a spoken sentence or chat message) made by an individual within a virtual meeting, they require close attention to levels of analysis. For example, if a researcher were to collect data from 100 60-minute group meetings, this would yield 6000 minutes of recorded speech. With respect to challenges, granular data often requires researchers–who may be used to smaller sample datasets–to contend with incredibly large volumes of information. This enables deriving insights into who talks to whom and how different people may communicate with one another in distinct ways. With respect to opportunities, researchers can use virtual meetings to track the flow and content of communications on a second-by-second basis. ![]() With this granularity comes both opportunities and challenges. ![]() Virtual meetings afford granular data on how people communicate with one another. ![]()
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