Apple SpeechAnalyzer beats Whisper Small in an on-device benchmark

Apple's SpeechAnalyzer is starting its life with a useful independent data point. Inscribe ran Apple's new speech API, the older SFSpeechRecognizer, and three WhisperKit CoreML models through the same LibriSpeech test sets on an Apple M2 Pro. The headline result is unusually clear: SpeechAnalyzer produced a 2.12% word error rate on test-clean and 4.56% on test-other.

That puts it ahead of Whisper Small in this setup. Whisper Small came in at 3.74% and 7.95%, while the legacy SFSpeechRecognizer trailed badly at 9.02% and 16.25%. Inscribe also reported that SpeechAnalyzer ran at roughly three times the speed of Whisper Small while avoiding the need to bundle a roughly 460MB model.

For app developers, the practical question is less about a model leaderboard and more about migration cost. If an iOS or macOS app still uses SFSpeechRecognizer for anything beyond short commands, this benchmark suggests the new API can reduce transcription errors without adding a separate model distribution problem.

The HN discussion centered on the missing numbers from Apple, the limits of LibriSpeech as a proxy for real-world audio, and whether the advantage will hold for multilingual or domain-specific speech. Those caveats matter. A clean benchmark is not the same as a noisy meeting room, a call center, or a medical dictation workflow.

Even with that restraint, the result changes the default assumption for English on Apple hardware. Whisper remains valuable for portability and control, but SpeechAnalyzer now looks like a serious built-in baseline rather than a convenience API.