Ice’s Post: https://blogs.ubc.ca/iceetec540/2025/09/21/task-3-voice-to-text-task/

In my Task 3 post, I recorded an unscripted story about a recent softball game using Speechnotes and then reviewed how the transcript captured (and sometimes misrepresented) my spoken language. The tool produced several misheard words, long run-on sentences, and a conversational tone that reflected how I naturally tell stories aloud. Ice completed the same task and shared a spontaneous story about taking a late bus to school after their car would not start. Their transcript shows similar issues such as incorrect vocabulary, filler phrases, and a lack of punctuation. Both of our posts explore how voice-to-text tools transform speech into text while preserving the informal qualities of oral storytelling.

---

Why I Chose This Link

I chose Ice’s post because both of our stories highlight the tension between spoken language and the written form that voice-to-text software produces. While my story focuses on a recreational moment from a softball game, Ice’s story describes a stressful morning and the pressure of unexpected delays. Even though the situations are very different, the technology captures our speech in comparable ways. This connection allows me to see how spontaneous storytelling is shaped by the tool. It also reinforces that transcription issues are not simply user mistakes but part of how the software interprets and reshapes oral communication.

---

Authoring Tool and Interface

Both of us relied on accessible voice-to-text applications. I used Speechnotes. Ice used a similar real-time transcription tool. Because neither of us dictated punctuation, our transcripts appear as long streams of text. This reveals how the interface guides the output. Voice-to-text tools tend to privilege the flow of speech rather than the structure of conventional writing. As a result, both of our transcripts highlight how much editorial work is usually required to convert spoken ideas into readable written text.

---

Privileged Literacies and Theoretical Connections

Our work in Task 3 foregrounds oral literacy. It brings attention to the pauses, fillers, corrections, and hesitations that are natural in spoken communication. These are often invisible in traditional writing but become highly visible when converted directly into text. The task connects to theoretical ideas about orality and literacy, especially the ways in which oral language resists the fixed conventions of writing. It also shows that writing technologies shape meaning. What the software hears becomes the text, even if it alters intention or clarity.

---

How Our Experiences Differ and What I Learn

My story followed a clear sequence of events because the softball game provided a natural structure. Ice’s story felt more unpredictable and reactive because of the stress of the moment they were describing. Despite these differences, we both discovered that the technology flattened our stories into similar textual patterns. From Ice’s post, I was able to observe how voice-to-text can carry emotional tone into the transcript, even when the words are sometimes inaccurate. This connection helps me reflect on my own transcript not only as a technical output but also as a representation of how I speak and think in the moment.