Our new utterances feature segments speech into meaningful semantic units, which allows the chosen model to interact more naturally and effectively with speakers’ spontaneous speech patterns. For example, when humans speak to each other conversationally, they often pause mid-sentence to reformulate their thoughts, or stop and restart a badly-worded sentence. When
utterances is set to
true, these utterances are identified, so you can present a transcript with a more natural speaking pattern.
The feature can be useful for performing analytics on finer segments of a conversation, say for sentiment analysis, or for presenting a transcript in a more readable format.
To learn more about the feature, check out the feature guide in our Documentation.