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Android documentation lists Hindi as a supported beta locale for Google ML Kit speech recognition, but that does not show whether a recognizer can accurately transcribe Hindi and English mixed in one utterance. No handset, audio samples, or transcripts are available here, so there are no device-level accuracy results to report. A meaningful test must identify the recognizer and settings, verify model downloads and offline behavior, and score the same mixed-language speech across configurations.
What Android’s current documentation establishes
Google’s ML Kit GenAI Speech Recognition API documents two modes. Its locale tables list Hindi (hi-IN) as beta for both. Google cautions that beta languages may have a slightly higher word error rate and recommends evaluating the intended use case. The documentation does not publish a Hindi-English mixed-speech benchmark or guarantee how mixed speech will be transcribed or which script it will use. These details are from Google’s documentation accessed October 3, 2026; availability and API details can change.
| ML Kit mode | Documented device eligibility | Hindi status | What the documentation does—and does not—show |
|---|---|---|---|
| Basic | Android devices using API level 31 and higher | hi-IN is beta | Traditional on-device recognition; the locale listing does not establish Hindi-English mixed-utterance accuracy. |
| Advanced | Pixel 10 and Pixel 11 | hi-IN is beta | Uses an on-device Gemini model. The documentation describes Advanced mode as alpha, with no SLA or deprecation policy; it does not establish mixed-speech accuracy. |
These are ML Kit modes, not a guarantee that every Android speech-to-text feature or app uses them. An app may use a different recognition service, so a result is meaningful only when the recognizer and mode are identified.
Does Hindi support mean Android can transcribe Hinglish?
No such conclusion follows from a Hindi locale listing alone. Code-switching means using more than one language within a sentence or discourse. A recognizer tuned for one language may handle an occasional word from another language yet struggle when switching is frequent. The Hindi-English corpus paper by Ganji Sreeram, Kunal Dhawan, and Rohit Sinha identifies variant English pronunciations and code-switching effects as sources of difficulty; it is context on the task, not an evaluation of modern Android models.
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Published figures also need to stay attached to the systems and datasets that produced them. Chadha et al. reported a word error rate (WER) of 21.77 for their proposed method on a Hindi-English code-switching task in 2022. A 2021 multilingual and code-switching ASR challenge paper describes approximately 600 hours of transcribed data across its languages and tasks and reports a 32.45% baseline WER on its code-switching test sets. Neither figure is an Android phone result, and the figures should not be compared as if they came from one system, dataset, or scoring setup.
How to run a useful on-device test
A reproducible evaluation compares outputs from the same speech, not impressions from unrelated dictation attempts. Record the configuration and preserve each original audio sample and transcript.
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- Identify the recognizer and device. Record the handset model, Android version and build, recognition service or API, selected ML Kit mode if applicable, and the locale settings. Do not label a result simply “Android speech recognition.”
- Check model readiness. For ML Kit, check feature status and download the required model if necessary before starting recognition. Record whether the model was available and downloaded. A locale appearing in documentation does not by itself show that the model is ready on a particular phone.
- Use a controlled input. Record whether speech came from the microphone or an audio file descriptor. For custom audio with ML Kit, the integration guide specifies raw, headerless, 16-bit PCM, mono audio at 16 kHz, supplied in real time. Keep capture conditions consistent when comparing recognizers.
- Test language switching deliberately. Keep the selected locale and any language-detection or switching settings in the test record. The ML Kit examples use single-locale examples; the documentation reviewed does not guarantee that one utterance can freely alternate between Hindi and English. Do not infer that behavior from the hi-IN listing.
- Score and inspect the output. Save the exact transcript, including whether Hindi appears in Devanagari or Roman script, and record latency, no-match responses, and errors. Use a reference transcript and a stated scoring method, such as WER, and report the utterances, speakers, accents, noise conditions, and code-switch points tested. A short sample cannot establish broad performance across speakers or environments.
How to check language switching and offline operation
Android’s platform RecognizerIntent provides optional language-detection and automatic language-switch parameters beginning at API level 34. Android’s API reference says the corresponding language models must be downloaded, and warns that a parameter may have no effect depending on the recognition-service implementation. These platform parameters are not proof that a specific app or service will switch successfully.
The platform also has a “prefer offline” parameter, but Android warns that its effect depends on the implementation. Treat it as a request, not confirmation of offline recognition. To verify offline operation, prepare the required models, disconnect the network, then run the same test with the exact recognizer and settings recorded. Report whether recognition actually succeeds; do not claim that data stays on the device based only on an on-device mode label.
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What a fair comparison should report
If comparing modes, devices, or services, keep the utterances and evaluation method constant and report the differences that can change the result:
- Supported Hindi locale and whether it is beta or production.
- Device model, Android/API level, recognizer, and mode eligibility.
- Model availability and download state, plus network-connected or disconnected status.
- Language detection or switching settings and whether they changed the output.
- Transcript correctness, script rendering, latency, no-match behavior, and errors.
- Speaker and accent coverage, recording setup, and noise conditions.
Keep published research benchmarks separate from phone measurements. WER results are only meaningfully comparable when the datasets and scoring conventions are aligned.
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Sources and scope
Product behavior and API qualifications above are based on Google for Developers’ “GenAI Speech Recognition API | ML Kit for Android” and Android Developers’ “RecognizerIntent” and “SpeechRecognizer” API references, accessed October 3, 2026. The code-switching context and research figures come from Ganji Sreeram, Kunal Dhawan, and Rohit Sinha’s “Hindi-English Code-Switching Speech Corpus” (2018); Harveen Singh Chadha et al.’s “Code Switched and Code Mixed Speech Recognition for Indic languages” (2022); and “Multilingual and code-switching ASR challenges for low resource Indian languages” (2021). These sources do not provide a hands-on result for a particular Android handset.
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