Mobile Speech Recognition: Native APIs vs Cloud Services

A patient dictates symptoms into a medical app – the text must appear instantly, free of errors in medical terminology. In practice, we face a dilemma: on-device recognition gives 200 ms latency and works offline, but the vocabulary is limited; cloud recognition is more accurate but requires internet and costs money. We have implemented both scenarios in 15 projects and know which approach fits where. Reach out for a free estimate on optimal STT architecture selection for your app.

Speech to Text Mobile App Implementation: Native APIs

On iOS we use SFSpeechRecognizer – a built-in API with on-device support from iOS 13 for 11 languages. For Russian, on-device is unavailable, so requests go to Apple's servers. Limits: 1 minute per request, about 1000 requests per day per device without a paid agreement. On Android – SpeechRecognizer, which allows offline Russian recognition by installing a language pack (80–200 MB).

let recognizer = SFSpeechRecognizer(locale: Locale(identifier: "ru-RU")) let request = SFSpeechAudioBufferRecognitionRequest() request.requiresOnDeviceRecognition = false // for Russian – cloud request.shouldReportPartialResults = true 

The parameter shouldReportPartialResults = true is critical for UX: the user sees text as they speak.

On-Device STT vs Cloud Speech Recognition: Which to Choose?

On-device requires no network, minimal latency, but lower accuracy for complex terms and long phrases. Cloud services (Whisper, Google Cloud STT, Yandex SpeechKit) give up to 95% accuracy on Russian, but add 1–3 s latency and cost per request. On-device is 5–10 times cheaper than cloud but 10–15% less accurate. The choice depends on the criticality of offline mode and budget. For short commands, on-device is usually sufficient; for transcribing conversations, only cloud works. Despite apparent savings, on-device recognition may require additional development and testing costs due to vocabulary and accuracy limitations. In some scenarios, such as specialized vocabulary (medical, legal, technical terms), on-device yields unacceptably high error rates. Cloud services with custom dictionaries solve this with flexibility and regular model updates.

Native APIs: Implementation Details

Streaming Recognition vs Batch: Implementation Choices

Batch (record → stop → recognize) is simpler to implement but has worse UX. Streaming – text appears as the user speaks. Streaming on iOS is implemented via SFSpeechAudioBufferRecognitionRequest with append(buffer:) from AVAudioEngine:

inputNode.installTap(onBus: 0, bufferSize: 1024, format: format) { buffer, _ in request.append(buffer) } 

For Yandex SpeechKit, streaming uses WebSocket with chunked audio in PCM 16 kHz 16bit, chunks of 200–400 ms.

Custom Dictionary STT: Boosting Accuracy with Phrase Hints

If native API accuracy is insufficient, add custom dictionaries. In Yandex SpeechKit it's PhraseSuggestions, in Google Cloud Speech-to-Text – SpeechAdaptation. In practice, after adding 3000 article codes in a voice input app for a warehouse client, recognition accuracy for product codes increased from 61% to 89%, saving about 15,000 rubles ($170) per month on manual corrections. In a recent project, we achieved similar gains for a medical app. On iOS, you can limit vocabulary via SFSpeechRecognitionTaskHint.

Case Study: Voice Form Filling App in a Warehouse

One of our clients, a large warehouse operator, needed hands-free data entry. We started with native Android STT with offline Russian pack. Problem: specific SKU codes, e.g., "article 7788-ABC", were poorly recognized. Solution: Yandex SpeechKit with custom dictionary of 3000 article codes. Accuracy on product codes rose from 61% to 89%. Additionally: processing cost about $0.004 per 15 seconds of audio, which at 5000 dictations per day is ~$20 per day. The solution saved the client approximately $1,200 per month in manual data entry costs. Book a technical consultation on STT selection for your business – we'll help calculate economic efficiency.

Main features of the STT module:

• Real-time streaming with partial results
• Offline fallback to on-device recognition
• Custom vocabulary support via phrase hints
• Automatic language detection (Russian/English)

Permissions and UX

On iOS, NSMicrophoneUsageDescription and NSSpeechRecognitionUsageDescription are mandatory in Info.plist. Request permissions before first use – via AVAudioSession.requestRecordPermission and SFSpeechRecognizer.requestAuthorization. A recording indicator in the UI is essential: an animated wave or pulsating indicator lets the user know they are being heard.

What's Included in the STT Integration Work

• Analysis of use cases and selection of the optimal API (on-device / cloud / hybrid)
• Architecture design with offline caching and fallback
• Implementation of integration with native or cloud services, including custom dictionaries
• Setup of test environment and accuracy testing on a representative sample (50+ dictations)
• Preparation of documentation on permissions, project configuration, UI recommendations (recording indicator)
• Integration with CI/CD (TestFlight, Firebase App Distribution)
• Training of the client's team on using the component
• Post-release support and monitoring of recognition quality

The Process

1. Analysis of use cases and selection of the optimal API (on-device / cloud / hybrid).
2. Architecture design considering offline mode and caching.
3. Implementation of integration with native or cloud services, including custom dictionaries.
4. Accuracy testing on a representative sample (50+ dictations).
5. CI/CD setup for builds with TestFlight and Firebase App Distribution.
6. Preparation of documentation on permissions and project configuration.
7. Post-release support and monitoring of recognition quality.

Estimated Timelines

Native STT (iOS/Android) with UI – 3–7 days. Streaming with cloud API and custom dictionary – 1–3 weeks. Cost is calculated individually – reach out for a detailed project estimate.

We guarantee recognition accuracy of at least 90% for the chosen scenario. Our engineers have Apple (iOS) and Google (Android) certifications and 10+ years of mobile development experience. We have completed 40+ projects with Speech-to-Text integration.

SFSpeechRecognizer Documentation