A transcript records what was said. The harder language work begins when you need to understand why a speaker switched between Twi and English, what a phrase means in context, and which follow-up question to ask.
At 6:40 p.m. in Kumasi, an invented composite interviewer named Efua watches the red light go out on a recorder packed with a full afternoon of interviews. Her notebook has a bent corner, three names circled twice, and one sentence underlined: “Nanso, the money deɛ…” She needs to brief her research partner that evening. If she misreads that turn in the conversation, tomorrow’s interview guide will pursue the wrong issue.
The audio can become text. That still leaves Efua staring at a sentence whose meaning lives partly in its language switch.
A transcript can preserve words and still lose the point
Speech-to-text solves a demanding first problem: turning audio into words that can be searched, quoted, compared, and reviewed. For bilingual interviews, accuracy also depends on recognising where one language ends, where another begins, and where both are working together.
Yet a clean transcript does not automatically explain the interview.
Efua’s underlined sentence contains a turn. “Nanso” signals a contrast, while “deɛ” places emphasis around the money. The English words alone do not carry the whole movement of the thought. A reader could flatten the line into a neutral statement and miss the speaker’s hesitation, correction, or change of direction.
That matters because an interview is more than a container of sentences. It is a sequence of choices. A speaker may begin in English for a formal explanation, move into Twi when describing family pressure, then return to English for a term used at work. The switches help carry tone, distance, emphasis, and relationship.
This is why a recent r/Ghana request for an AI service that can transcribe Twi and Ewe interviews into English points to a larger need. Dependable transcription is essential. Researchers also need a way to continue thinking with the language after the recording has been converted into text.
Code-switching belongs inside the analysis
Efua first considers translating every Twi phrase into English and working only from that version. It would make the document easier to share. It could also erase the clues she needs most.
The interviewee did not choose one language and remain there. Efua should not have to force the analysis into one language either.
She opens Nkomo and types the excerpt as it was spoken, keeping the Twi and English together. Then she asks, in the same mix she would use with a colleague, what the contrast might be doing in that sentence and which interpretations deserve checking in the next interview.
Nkomo supports natural Twi and Ghanaian English conversation by text or voice. Efua can ask a short question in Twi, continue in English, or mix both in one turn. This gives her room to examine the passage without translating her own thinking before she begins.
The distinction matters. Nkomo should not be treated as proof of what an interviewee intended, and it does not replace review of the recording, transcript, or wider interview. It can help Efua discuss a difficult passage, test possible readings, and draft a follow-up question. The final interpretation still belongs to the researcher.
As the story of how “nanso” changed who Ama needed to call shows, a small connector can redirect the meaning of everything around it.
Privacy choices should come before sensitive discussion
Interview material may contain names, family disputes, health details, financial concerns, or opinions that were shared for a limited purpose. Language analysis cannot be separated from decisions about where that material goes.
Before Efua discusses an excerpt, she checks what it contains. She removes identifying details that are unnecessary for the question. In Nkomo, cloud consent is explicit: never, ask each time, or allow it for the current session. She chooses deliberately instead of assuming that every prompt should leave the device.
Conversation history is controlled on the device. If she turns history off, it is purged immediately. She can also export her data or delete her account with one tap. If something fails, the error appears rather than disappearing silently and leaving her to guess whether the request worked.
These controls do not make every interview excerpt appropriate to share. They make the decision visible. Efua still has to follow the consent terms, research protocol, and confidentiality promises attached to her work.
The next question is the real output
With the interview guide due that evening, Efua returns to the audio around the underlined sentence. She compares the recording with the transcript, reviews the surrounding exchange, and rejects an interpretation that sounded plausible when the sentence stood alone.
Then she rewrites tomorrow’s follow-up: ask what changed when money entered the decision, using the speaker’s own framing without putting an answer in their mouth.
At 7:15 p.m., the recorder is charging beside her notebook. The transcript now has a margin note beside “nanso,” followed by two possible readings and one question designed to tell them apart.
That is where the language work lands. Keep the mixed-language sentence intact. Remove details you do not need. Choose your cloud setting before entering sensitive text. Then use the conversation to prepare a better human question, and return to the recording for the answer.
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