You can add spoken prompts and learner responses to an offline language tutor, but speech synthesis and speech recognition have different requirements. Browser text-to-speech can use voices available on the device; browser speech recognition may send audio to a remote service unless a supported on-device mode is explicitly selected and its language pack is installed. For dependable local processing, package or provision a local speech engine and its required assets, then test on the devices and languages you support.
Choose an offline architecture first
Decide whether the tutor will run in a browser or in a managed desktop, mobile, or classroom app. That choice affects which recognition engines are available, how language assets are installed, and what you can promise about audio handling.
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| Approach | What it offers | What to verify |
|---|---|---|
| Browser on-device recognition | Uses the browser’s speech-recognition interface with local processing when the browser supports it and the requested language is available locally. | Browser and language support; language-pack availability; first-run download behavior; Permissions Policy; learner-device performance. |
| Local recognition engine | Engines such as whisper.cpp and Vosk provide documented offline recognition paths; whisper.cpp also documents microphone streaming. | Integration effort, model and language coverage, resource use, interaction latency, and recognition quality on the actual target devices. |
These are implementation options, not a quality ranking. The project documentation does not establish which engine will work best for a particular language, dialect, learner group, or minimum device.
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Implement browser speech recognition with an explicit local path
Do not assume that using the Web Speech API makes recognition offline. MDN describes the default behavior this way: “Your audio is sent to a web service for recognition processing, so it won’t work offline.” Its documented on-device option is conditional: the browser must support the local-processing methods, and the relevant language pack must be available on the device.
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- Check browser capabilities. Detect the recognition interface, including a prefixed interface where needed, and check for the on-device processing and language-availability methods. Browser speech recognition has limited availability; the local-language methods are experimental. If a required capability is missing, show an unavailable state rather than silently using remote recognition.
- Start from a clear learner action. Use a button such as “Record answer” so the learner knows when capture begins. Request microphone access as needed and explain permission denial in the interface.
- Set the recognition language. Assign the appropriate BCP 47 language tag to the recognizer’s language property. Explicit language selection matters when the tutor supports more than one language.
- Request local processing where supported. Set the recognizer’s processing preference to on-device before starting. Check whether the chosen language is available locally; when the browser reports that it can be downloaded, install the language pack and then let the learner retry.
- Handle results and failures. Read the transcript from the recognition result event and present it for review. Treat no match, microphone denial, unavailable language, installation failure, and recognition errors as normal states with useful next actions.
The local recognition methods can also be blocked by the on-device-speech-recognition Permissions Policy. Check this policy in deployments that embed the tutor or use cross-origin frames; a policy restriction can prevent language availability or installation attempts from working.
Use a local recognition engine when browser support is not enough
For a controlled application or classroom deployment, an engine integrated with the app can make the speech stack less dependent on browser-specific support. whisper.cpp documents offline, on-device inference, CPU and accelerator paths, and a real-time microphone-input example. Vosk describes an offline recognition toolkit with a streaming API and configurable vocabulary.
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Before choosing, test each candidate with the exact target language, dialect, script, learner speech, and oldest supported device. Measure the things the project pages do not settle for your application: transcript usefulness, latency, memory and processor load, and whether live or submitted-clip interaction is practical. Do not infer a comparative winner from the fact that an engine supports offline operation.
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Add text-to-speech separately
To make the tutor speak a prompt or model phrase in a browser, create a SpeechSynthesisUtterance, set its language, optionally select an appropriate available voice, and pass it to the device’s speech synthesizer. A minimal example is:
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const utterance = new SpeechSynthesisUtterance("Bonjour, comment ça va ?");
utterance.lang = "fr-FR";
speechSynthesis.speak(utterance);Available voices vary by device and installed speech service. Enumerate the voices the browser reports and offer a meaningful choice when more than one suitable voice exists; avoid promising a particular voice on every learner’s device. The browser’s speech-synthesis interface is distinct from recognition, so its local voice behavior does not make recognition local.
If the tutor needs a controlled local voice rather than whatever the device provides, Piper is a project-described local neural text-to-speech option. Confirm that its voice assets cover the required language and that the assets and software licenses fit the way the tutor will be distributed.
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Design feedback for learning, not just transcription
Show the recognized transcript before scoring or moving on. Give learners a way to replay the reference phrase, retry recording, and compare their response with the target text. A transcript tells you what the recognizer inferred; by itself it does not measure accent, phoneme quality, or fluency. Do not label transcription matching as pronunciation assessment unless you have a separately validated scoring method.
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Make the offline boundary clear
Separate setup from ordinary offline use. Browser on-device recognition may require a one-time language-pack download before the device can recognize speech locally. A packaged recognition engine or local TTS system likewise needs its model and voice files present; if the installer has not provisioned them, first-time setup may require connectivity. Explain whether captured audio stays on the device, and disclose any service that receives audio. Do not fall back to remote recognition behind an offline or privacy promise.
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- State which supported browsers, app platforms, and languages have local recognition.
- Tell learners whether language assets download during setup and what to do if they are unavailable.
- Offer a typed response or a clear retry path when microphone access, local recognition, or a language pack is unavailable.
- Verify behavior with the target language, representative consenting speakers, real microphones, and the oldest supported hardware.
A headset with a microphone may help in a shared or noisy room, but it is optional; an existing device microphone may be sufficient. Test the tutor with the audio hardware learners are likely to use.
Quick Recap
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