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To add voice to an offline language tutor, treat speech recognition and text-to-speech as two separate features. Browser speech synthesis can read prompts using voices available on the device. Browser speech recognition may send audio to a remote service by default; offline recognition requires a supported on-device implementation and the right language pack, or an engine and model you distribute locally.
Choose the speech setup that matches your app
Start with the deployment requirement, not the API name. The Web Speech API includes separate recognition and synthesis interfaces, and local behavior in one does not make the other local.
| Approach | Offline behavior | What to verify |
|---|---|---|
| Browser recognition with on-device processing | Can recognize locally after the required language assets are available, in browsers that support the feature. | Browser and language support, language-pack availability, permissions, and first-run download behavior. |
| Packaged local recognition engine | Can process locally when the application has the engine and required model files installed. | Target-language quality, device performance, model distribution, and interaction style. |
| Browser speech synthesis | Uses speech services and voices available on the device; availability varies by environment. | Suitable voices and language support on every target device. |
| Packaged local TTS engine | Can generate speech locally when the engine and voice assets are present. | Voice quality, language coverage, platform fit, asset licensing, and distribution. |
For browser recognition, MDN warns that the default web-page recognition flow sends audio to a web service and “won’t work offline.” Its documented on-device option is conditional: the browser must support it and the requested language pack must be available locally. MDN’s Web Speech API guide describes both behaviors.
Implement browser-based on-device recognition
Use feature detection rather than assuming that all browsers expose the same recognition interface. Some implementations use a prefixed interface, and the on-device language availability and installation methods have limited or experimental support. MDN also documents that Permissions Policy can block those methods.
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- Check support. Detect the available recognition interface and the local-processing methods you need. If they are absent, show a clear unavailable state rather than silently switching to a remote recognizer.
- Start from a learner action. Use a button such as “Record answer” to request microphone input, so the learner knows when capture begins.
- Set the language explicitly. Assign the appropriate BCP 47 language tag to the recognizer for the exercise. Recognition results include transcripts and may provide alternatives.
- Request local processing. Where the browser supports it, select on-device processing before recognition starts.
- Check language-pack availability. Use the browser’s availability method for the requested language. If the pack can be downloaded, install it and tell the learner what is happening; let them retry after installation.
- Handle ordinary failure states. Provide useful responses for an unavailable language, denied microphone permission, no match, and recognition errors. Let learners retry or type an answer.
- Test the embedding policy. If the tutor runs in an iframe or cross-origin context, configure the documented
on-device-speech-recognitionPermissions Policy as needed. A blocked policy can make availability or installation attempts fail.
Do not promise that first-time setup is offline if it downloads a language pack. Describe the boundary accurately: recognition can run offline after the required assets are available locally, but setup may require a connection.
Use speech synthesis to read tutor text aloud
Speech synthesis is independent of recognition. In a browser, create a SpeechSynthesisUtterance, set its language to match the phrase, optionally select a suitable available voice, and submit it to the SpeechSynthesis interface. The browser uses the device’s speech service; voices can differ between operating systems and may not be installed for every language.
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Enumerate available voices and offer a meaningful choice when more than one suitable voice exists. Avoid hard-coding a voice name as if it were guaranteed on all devices. Use synthesis for prompts, model pronunciations, and example answers, and give learners a replay control.
If you need a controlled local TTS stack rather than the device’s speech service, Piper is a project-described local neural text-to-speech option. Before adopting it, check current maintenance and releases, the voice assets available for your language, platform fit, and the licenses relevant to your distribution. Having a local engine is not enough by itself: the required voice files must also be present on the device.
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For details on the browser interfaces, see MDN’s Web Speech API reference.
Use a local engine when browser support is not enough
For a desktop, mobile, or managed classroom app that needs predictable local processing, evaluate an engine you can package and control. Two documented candidates are whisper.cpp and Vosk. Their project descriptions establish possible implementation routes, not a comparative winner for a particular language or device.
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- whisper.cpp: Its project documentation describes on-device inference, CPU and accelerator paths, and microphone-streaming examples. The model and device combination still needs to be tested on your minimum supported hardware.
- Vosk: Its project documentation describes an offline recognition toolkit with a streaming API and configurable vocabulary. Confirm that its language and deployment options fit your exercises.
For either option, include the necessary model files in the install or provision them before the learner goes offline. Measure resource use and response time on your oldest supported device; the cited project pages do not establish accuracy, latency, or memory figures for your tutor’s language and hardware.
See the project documentation for whisper.cpp and Vosk.
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Give feedback without overstating what recognition measures
Show the transcript before scoring or correcting an answer. A speech recognizer returns text; that text alone does not establish whether pronunciation, accent, individual phonemes, or fluency were good. Do not present transcription as a pronunciation-quality score unless you build and validate a separate method for that purpose.
- Let learners replay the reference phrase.
- Show the recognized transcript and make retries easy.
- Allow typed answers when recognition is unavailable or unsuitable.
- Test with consenting speakers representative of the target language, dialect, and learner population before using transcripts for assessment.
Validate language coverage, privacy, and device behavior
Before selecting an implementation, test the actual target languages, dialects, scripts, learner speech, and minimum devices. Compare offline guarantees, platform coverage, streaming versus push-to-talk interaction, resource demands, intelligibility, and usefulness of the resulting transcript or voice. The available project descriptions do not provide a like-for-like quality ranking for a language tutor.
State plainly whether audio stays on the device. Do not send captured audio to a service without telling learners. If browser recognition is unavailable and the product promises offline use, offer a local engine path, typed response, or a clear message that voice input cannot be used in that environment—never an undisclosed remote fallback.
A headset with a microphone can help in a shared or noisy room or when a device has a poor microphone, but it is optional; an existing device microphone may be sufficient.
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