Turn English audio
into Twi (Akan) text.

A different approach to English to Twi (Akan) translation.

English to Twi (Akan) translation turns English audio, video, or text into Twi (Akan), the language spoken by close to 9 million people, primarily in Ghana and parts of Côte d’Ivoire. Twi is part of the Kwa language group and is tonal, using pitch to distinguish meaning, which is part of why word-for-word tools miss so much. Businesses expanding into Ghana, researchers working with Akan-speaking communities, and NGOs running outreach programs have relied on accurate translation for years to reach the people English alone does not.

Why English-to-Twi translation breaks down

For most teams, translation meant sending a document or recording to a freelance translator and waiting days for it to come back, with no fast way to check whether the tone or a speaker’s intent survived the trip. Idioms and cultural nuance were usually the first things lost. A single mistranslated phrase in a training video or a support script could cost the trust of an entire Akan-speaking audience, and there was no quick way to catch it before it shipped.

Reading the audio, not just the words

Speak AI treats every English file the way a skilled interpreter would. Audio and video are transcribed first, in your language, with 100+ supported, then translated into Twi (Akan) with speaker names and timestamps kept intact, so the output still reads like a conversation, not a wall of text. Because the underlying recording is analyzed, not just the transcript, the tone, energy, and emphasis in the original English carry through into how the Twi (Akan) translation is structured, and for video, the visual context, slides, captions, and who is speaking on screen, stays lined up with the translated transcript.

Ask across your translated library with AI chat, using ChatGPT, Claude, and Gemini built in, and track translation volume and turnaround with dashboards you can customize and white-label, so this quarter’s outreach is measured against last quarter’s.

What teams ask their Twi (Akan) translations

• “Translate this outreach script into Twi (Akan) and keep the speaker names.”
• “Which training sessions this month were translated, and how long did each take?”
• “Flag any English idiom in this recording that might not translate directly.”
• “Show me every Twi (Akan) translation from this quarter’s outreach calls.”
• “Summarize the feedback from Akan-speaking participants across all sessions.”

From a document backlog to a translated library

The result is a translation workflow that keeps up with how much English content your team actually produces. Files translate in minutes instead of days, speaker names and structure carry over instead of flattening into one block of text, and a searchable library replaces a folder of one-off documents sent to different translators. One multilingual assessment program used this workflow to scale interpreting and translation across an embedded-recorder deployment, and you can read how in the Interpreting.com multilingual case study.

And because Twi (Akan) translation rarely lives alone, the same engine scores calls, meetings, and training sessions on the same criteria, connecting translation to call scoring and the MCP server that puts your whole translated library inside Claude, ChatGPT, and Cursor.