Does cloud speech recognition store my audio?

Policies vary from provider to provider, and they change over time. The safe assumption is that anything you dictate to a cloud service may be stored, reviewed, or used to train models. On-device recognition avoids the question entirely because there is no upload.

Who could hear my recordings with cloud speech recognition?

Beyond the service provider, recordings can be accessed by employees and contractors, and may flow into model training pipelines. Exactly who has access depends on the company's policies, which is why the on-device alternative is appealing: nothing is ever transmitted.

Can on-device speech recognition work without internet?

Yes. The recognition model is bundled on the device, so recording, transcription and decoding happen locally. Veena transcribes entirely on-device and needs no internet connection.

Is on-device speech recognition less accurate than cloud?

Modern on-device models are optimized with techniques such as quantization to fit on phones and laptops, and the practical accuracy difference is often small. Veena does not publish benchmarks; the reliable test is to dictate real sentences and compare.

Cloud vs On-Device Speech Recognition: Why Privacy Matters | Veena - Offline Speech to Text for 22 Indian Languages

Every speech to text app falls into one of two camps. Cloud speech recognition sends your audio to a server, where it is transcribed and sent back. On-device speech recognition runs the model on your phone or computer, so the audio never leaves your device. The difference sounds technical, but it decides who can hear your recordings. This article explains how each approach works and why privacy is the practical reason to care.

What cloud speech recognition does

When you dictate into a cloud app, the app records your voice and uploads it, usually in chunks as you speak, to the company’s servers. A large model transcribes it there, and the text comes back to your device. This is convenient: the model can be huge, and the company can keep improving it. But it comes with consequences. Your audio is now on infrastructure you do not control. It may be stored for a while, used to train or tune models, reviewed by employees or contractors, or shared within a broader company group. Every provider handles this differently, and policies change over time. None of it requires anything beyond the terms you accepted when you installed the app.

The practical question is not whether a specific company is trustworthy today. It is whether you are comfortable with the idea that anything you dictate is a recording in someone else’s hands: a private message, a meeting with legal or medical content, an interview with a source, a voice note about your health. For sensitive material, the risks are not theoretical. Server breaches happen, and data used for training is hard to delete even when a provider promises to do so.

What on-device speech recognition does

An on-device system bundles a recognition model inside the app. Recording, feature extraction, inference and decoding all happen locally on your phone or computer. There is no upload, because there is nowhere to upload to: the model is already on your device. This has three practical consequences.

First, privacy. Your audio never leaves your device, so there is no recording on a server to leak, subpoena, or sell. Second, reliability. Transcription works with no internet connection, on a train, in a building with bad signal, anywhere. Third, speed. There is no round trip to a server, so the transcript appears as you finish speaking.

The trade-off is that the model has to fit on your device. That is why on-device models are quantized and optimized: smaller, faster versions of full-size models. And it is why covering many languages on-device is a genuine engineering achievement rather than a marketing line.

Why this matters in practice

Voice is intimate in a way that typed text is not. The way you speak carries tone, hesitation, and context, and recordings capture all of it. When you dictate into a cloud app, you are sending that to a server. On-device recognition keeps it where it belongs: on your phone. This matters for journalists protecting sources, for lawyers and doctors handling client details, for managers discussing salaries and strategy, and for anyone who dictates personal messages. Connectivity matters too: cloud tools fail in bad network areas, while offline transcription works anywhere, which is a daily reality for many users in India.

The scenarios are easy to find in everyday Indian life. A journalist dictating notes from a sensitive interview, a doctor recording case notes in Tamil, a teacher keeping assessments in Hindi, a family lawyer summarising a dispute in Telugu: in all of these, the difference between on-device and cloud transcription is the difference between a private record and a recording that exists elsewhere. Once audio is on a server, you cannot un-send it, and you have no way to verify how long it stays or who can retrieve it.

Veena: no account, no cloud

Veena is built around the on-device model. Its recognition engine, IndicConformer-600M-Multilingual by AI4Bharat, is exported to ONNX and quantized so it runs entirely on-device, covering all 22 scheduled languages of India with a dedicated vocabulary and decoding for each. The product promise is simple: free, no account, no cloud, works fully offline. “Your audio never leaves your device” is not a slogan but an architectural fact: there is no server to send it to.

Because there is no account, there is nothing to sign up for, no credentials to manage, and no profile that links your recordings to you. The app asks only for microphone permission. You can read about the philosophy behind the project on the about page, the mechanics of the apps on the features page, and pick your language on the speech to text hub.

The bottom line

If your recordings contain anything you would not post publicly, the architecture of your speech to text tool matters. Cloud recognition means your voice lives on a server; on-device recognition means it does not. Modern on-device models handle Indian languages well, so choosing privacy no longer means giving up quality. It is simply the default you should expect.