Nowadays, automatic speech-to-text recognition and manual unwinding are the main ways to get a transcription from a recorded audio. Speech-to-text recognition is fast and increasingly precise, but it has limits in the restitution of a narrative content, not providing indications in emotional terms. The unwinding allows a more qualitative analysis of a text/audio, but it is a slow and expensive process.
In order to provide technological support to the physical action of the story-finder, we designed a model divided into steps, with the aim of obtaining qualitative and narrative data from voice recognition.
Phase 1
During the interview, the story-finder puts keyframes of fixed duration (2-3 minutes). The keyframes are parts of the entire story that become autonomous micro-stories,
with narrative value and - if possible - a self-concluding sense. Each micro-story is an agglomeration of the words that composed it.
Phase 2
Each micro-story is analyzed by voice recognition, enhanced by the addition of three parameters: Time, Narrator and Emotion.
• Time
Compared to the time x of the interview, what time y does the narration refer to? The “present” axis is not at the center because - based on field research results - it emerged that memories are prevalent within an interview.
• Narrator
Does the person talk about himself (selfdiegetic), his context (homodiegetic) or with an observer's eye (heterodiegetic)?
• Emotion
Towards which axes does the emotional substrate of the narrative tend? Emotions are shown as configurational situations, resonances of human behavior, not polarized and with a nuanced nature. The poles from which the infinite emotional nuances spring up concern the perceived sensation (vital- nefarious) and the temporal connotation (present-expected) of emotions. Time and narrator parameters come from Genette's studies on narratology ("Gerard Genette", 2019). The emotion parameter is a union between Lisa Feldman Barrett's neuroscientific studies (Della Rocca, 2019) and Umberto Galimberti's philosophical analysis (Galimberti, 2019).
Phase 3
Adding the three parameters, every word of each micro-story is categorized and expanded semantically. Each word will have a wide range of connections with other words and at the same time a more specific connotation depending on the parameter. All the micro-stories, divided by topic and categorized by parameters, create the Beyondstories database.

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