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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchGoogle lists Hindi (hi-IN) as a beta language for both modes of its ML Kit GenAI Speech Recognition API, but that does not establish that Android can accurately transcribe Hindi and English mixed in one utterance. No specific phone, audio sample, or transcript is available here, so there is no measured handset result to report. The answer for a particular device depends on the recognizer, settings, downloaded models, and how it handles the speaker’s language switches.
What Android and Google document
Google’s ML Kit GenAI Speech Recognition API offers two on-device modes. Its documentation lists Hindi (hi-IN) as beta for both, and warns that beta languages may have a slightly higher word error rate. Hindi being present in a locale list is evidence of documented Hindi support—not a promise that a recognizer will switch cleanly between Hindi and English in the same sentence.
| ML Kit mode | Documented device eligibility | Hindi status | What that does—and does not—establish |
|---|---|---|---|
| Basic | Android devices using API level 31 or higher | hi-IN is listed as beta | Traditional on-device recognition is documented; the locale listing does not guarantee Hindi-English mixed-speech accuracy. |
| Advanced | Pixel 10 and Pixel 11 are listed | hi-IN is listed as beta | Uses Google’s on-device Gemini model. The API documentation describes Advanced mode as alpha and says it has no SLA or deprecation policy; availability and support may change. |
These details are from Google’s GenAI Speech Recognition API | ML Kit for Android documentation, accessed October 3, 2026. Check that page and the API’s current feature status before relying on a listed mode or locale.
Language switching is not the same as Hindi support
A code-switched utterance uses two languages within a sentence or a stretch of speech. The ML Kit examples use single-locale examples; the documentation reviewed does not publish a Hindi-English mixed-speech benchmark or guarantee which script a mixed transcript will use.
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Android’s platform RecognizerIntent includes optional language-detection and automatic language-switch parameters starting at API level 34. Android 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 should not be treated as proof that a given ML Kit mode or another recognition service will switch languages as intended.
How to test a phone rather than infer its performance
A useful test reports the recognizer’s actual output against known reference transcripts. Keep the speech samples identical across options; otherwise a difference may come from the speakers or recordings rather than the recognizer.
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- Record the setup. Note the handset model, Android version or build, recognition service and API, selected ML Kit mode if applicable, locale settings, and whether the relevant on-device models were available and downloaded. For ML Kit, Google’s integration guidance says to check feature status and download the model if needed before recognition.
- Prepare reference speech. Use a set of representative Hindi-English utterances with verified transcripts. Record the intended writing system for Hindi and how English words should appear. Include different speakers, accents, code-switch points, and recording conditions if those matter to the intended use.
- Run equivalent trials. Keep the utterance set and input conditions consistent when comparing recognizers. Record whether each run used a microphone or supplied audio, whether the network was connected, and the recognizer’s exact settings.
- Preserve the output and failures. Save the transcript exactly as returned, including script, punctuation, missing words, and errors or no-match responses. Do not silently normalize a transcript in a way that hides script or word-recognition differences.
- Score against the references. Report the scoring method and the results for the tested utterances. If using word error rate (WER), state how words and scripts were tokenized and normalized; scores can be misleading when the reference and output use different writing conventions.
For custom audio through ML Kit, the documented input format is raw, headerless 16-bit PCM, mono, sampled at 16 kHz, supplied in real time. That constraint matters when comparing file-based tests with live microphone use.
How to verify offline operation
Android’s “prefer offline” parameter requests an offline recognition engine; the platform documentation cautions that its effect depends on the recognition-service implementation. A setting or API request alone therefore does not prove that a test ran offline.
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To verify a configuration, record which models were downloaded, disconnect the device from the network, and repeat the same recognition trial. Report the service, mode, and network state with the result. Describing a mode as on-device is not by itself evidence that every part of a particular data path remained on the device.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What published code-switching research can tell you
Hindi-English code-switching creates challenges beyond recognizing either language in isolation. The paper Hindi-English Code-Switching Speech Corpus describes how monolingual ASR can handle a few foreign-language words yet struggle when switching is substantial, with variant English pronunciations and code-switching effects among the difficulties. This is context for why a mixed-speech test matters, not a measurement of a current Android recognizer.
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Two published figures provide research context but must not be mistaken for phone results:
- Chadha et al.’s 2022 paper, Code Switched and Code Mixed Speech Recognition for Indic languages, reports a WER of 21.77 for its proposed method on its Hindi-English code-switching task.
- The 2021 paper Multilingual and code-switching ASR challenges for low resource Indian languages reports approximately 600 hours of transcribed data across the challenge’s languages and tasks, and a 32.45% baseline WER on its code-switching test sets.
Those values belong to their respective research methods, datasets, and scoring setups. They are not comparable Android measurements, and should not be used to predict a particular handset’s accuracy.
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Sources
- Google for Developers, GenAI Speech Recognition API | ML Kit for Android (documentation accessed October 3, 2026).
- Android Developers, RecognizerIntent and SpeechRecognizer API references (documentation accessed October 3, 2026).
- Ganji Sreeram, Kunal Dhawan, and Rohit Sinha, Hindi-English Code-Switching Speech Corpus (2018).
- Harveen Singh Chadha et al., Code Switched and Code Mixed Speech Recognition for Indic languages (2022).
- Multilingual and code-switching ASR challenges for low resource Indian languages (2021).
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