In the world of oral history, we often say that the “truth” of an interview lives in the space between the words. It’s in the waver of a voice when a narrator recalls a childhood home, the sharp intake of breath before a difficult revelation, or the protective armor of a sarcastic laugh.

As oral historians, our mission is to preserve the fullness of the human experience. However, as we increasingly turn to Artificial Intelligence (AI) to handle the grueling task of transcription, we face a new challenge: The Ghost in the Machine.

AI can give us the text, but it often misses the soul. Here is why oral historians must look beyond the automated page to preserve the emotional subtext of their work.

1. The Erasure of the “Meaningful Pause”

To an AI algorithm, silence is a vacuum to be filled or a glitch to be ignored. But in oral history, silence is a language of its own.

• The AI Gap: Most AI tools automatically strip out long pauses or mark them with a generic [silence] tag.
• The Human Reality: A ten-second silence might be the sound of a narrator processing a trauma they haven’t spoken of in forty years. By “cleaning” the transcript, AI removes the weight of that hesitation, making a difficult testimony appear effortless and flat.

2. The Trap of Literalism

AI is remarkably good at “Speech-to-Text,” but it is notoriously bad at “Intent-to-Meaning.”

• The Sarcasm Problem: If an interviewee describes a grueling work environment by saying, “Oh yeah, it was a real paradise,” the AI will dutifully record the word “paradise.”
• The Risk: A researcher fifty years from now, reading only the transcript, might take that statement at face value. Without the human ear to catch the irony, the narrator’s true critique of their lived experience is lost.

3. Filtering Out the “Punctuation of Life”

We often overlook paralinguistic cues—the sighs, the throat-clearing, the nervous tapping of a finger on a table, or the sudden burst of laughter.

• Non-Verbal Data: These sounds act as emotional punctuation. A sigh can signal resignation; a laugh can be a sign of resilience or a defense mechanism.

• The Machine’s Filter: Most AI transcription engines are programmed to “denoise” the audio, treats these vital human sounds as background interference to be deleted.

4. The Flattening of Dialect and Identity

AI models are often trained on “standard” accents. When a narrator speaks in a rich regional dialect, uses a blend of languages, or has a speech impediment, the AI often “corrects” their speech to fit the norm.

• The Cost: This isn’t just a typo; it’s an act of linguistic colonization. It strips the narrator of their unique voice and cultural identity, forcing their story into a homogenized box.

How to Work With the Machine (Without Losing the Ghost)

AI transcription is an incredible time-saver, but for the oral historian, it is only a first draft. To ensure the emotional subtext survives, adopt these “Human-in-the-Loop” practices:

• The “Empathy Pass”: After the AI generates the text, do a dedicated editing pass while listening to the audio. Your goal isn’t just to fix typos, but to add emotional metadata—tags like [voice breaks], [bitter chuckle], or [long, heavy silence].
• Preserve the Audio: Never treat the transcript as the “final” version of the history. Ensure that future researchers have easy, synchronized access to the original audio so they can hear the emotions the text fails to capture.
• The Researcher’s Memo: Always include a short introductory note in your file describing the “atmosphere” of the interview. Was the narrator tense? Did the mood shift when a certain topic was raised?
• The Bottom Line: AI can transcribe the lyrics, but it cannot hear the music. As keepers of the past, our job is to make sure the music is never silenced.