AI Voice Generation for E-learning

We start by asking whether you need voiceover alone or voiceover with subtitles. We also establish whether narration will be placed directly on slides or in a video within the course. This determines working formats, approval points and final checks.

We analyze the source video or audio, script, languages and timing constraints. We adapt the workflow to the deliverables the client needs: consistent audio files, a localized video or a complete course version. Material exchange details are agreed during the brief.

Clear responsibilities matter for LSP teams. The client supplies and approves translations and subject-matter decisions. We handle agreed TTS production, editing, integration and technical checks.

We prepare narration samples suited to the language, audience and training style. We compare voice quality, pace, emphasis and naturalness. Samples should include specialist terminology, product names, abbreviations and numbers.

The client approves the approach before the full set is recorded. This allows voice style and pronunciation issues to be resolved in a small sample rather than repeating production across many files. We document decisions and use them as a reference for subsequent versions.

Approved text should sound right when heard, as well as read well on a slide. We consider sentence length, abbreviations, units and number formatting. If the translation is much longer than the source, we agree necessary changes with the language team.

We record difficult words in a pronunciation guide. We can use pronunciation-friendly spelling or a pronunciation dictionary where supported by the tool. For example, KPI may require “kej-pi-aj” in Polish narration. We confirm the final pronunciation in an actual recording.

We keep TTS-specific working text separate from the text displayed in the course. Decisions about names and terminology should remain consistent across slides, audio, video and subtitles.

After receiving translations and sample approval, we generate narration in the agreed environment, such as ElevenLabs. We retain the agreed file structure, names and IDs so every segment can be linked to a slide or scene.

We edit recordings, adjust pauses and audio levels and check for omissions, repetitions and artifacts. We send recordings to the client for listening review and approval. Reviewers can identify pronunciation that is unsuitable in context even when the written text is correct.

For video content, we can share a preview in Frame.io. The client hears the voice, sees what the narration refers to and comments on specific moments. For other content, we agree a feedback list linked to recording IDs.

When feedback concerns one segment, we correct that recording and check its joins with the rest of the material. If an issue recurs, such as a product name mispronounced across several lessons, we update the shared pronunciation guidelines.

Within an agreed workflow, we can apply the correction across multiple files and regenerate the required segments. We still check context, inflection and the audible result rather than blindly replacing every occurrence. The revised set is submitted for approval again.

We integrate approved voiceover into video or directly into a course, for example in Storyline, Rise 360 or agreed H5P content. We adapt display durations, pauses and synchronization. Where subtitles are included, we also check consistency with narration and visuals.

The finished material goes to the client’s QA team or SME. For courses, we can use Review 360 or an agreed test environment; for video, Frame.io. Reviewers assess the message in full context, and we implement approved production corrections.

The final package can include WAV / MP3, narrated video or an updated course and its assets. We agree formats, file structure and delivery in advance so the materials can also support future training updates.