Speech to Text

Speech-to-text (also called automatic speech recognition, ASR) transcribes spoken audio into written text. See Text to Speech for the inverse direction (text to audio); the two are often paired in voice interfaces but solve opposite problems.

How modern STT works

Current STT systems are predominantly neural, end-to-end models (such as architectures descending from wav2vec or Whisper-style transformers) that map raw or lightly-processed audio directly to text, largely replacing older pipelines that separated acoustic modeling, pronunciation modeling, and a language model into distinct stages.

Streaming vs. batch transcription

• Streaming (real-time) — transcribes audio incrementally as it arrives, producing partial results with low latency; needed for live captioning, voice assistants, and real-time transcription during a call.
• Batch (offline) — transcribes a complete, already-recorded audio file, typically with higher accuracy and richer features (speaker diarization, more context for disambiguation) since it can look at the whole recording rather than a rolling window.

Choosing between them is largely dictated by the use case's latency requirement, not just a quality preference — a live captioning feature needs streaming even if batch would transcribe more accurately.

Accuracy factors

Real-world transcription accuracy depends heavily on: audio quality (background noise, microphone quality), accents and dialects a model was or wasn't trained on, domain-specific vocabulary (medical, legal, or technical terms uncommon in general training data), and multiple overlapping speakers. Custom vocabulary/phrase hints (supported by most major providers) can meaningfully improve accuracy on domain-specific terms without retraining a whole model.

Common pitfalls

• Assuming uniform accuracy across accents and demographics — published accuracy benchmarks are often measured on specific, non-representative populations; verify actual performance for the target user base before assuming a general benchmark applies.
• No handling for low-confidence transcriptions — presenting a transcript as if it were certain, with no way to flag or correct likely errors, misleads users in high-stakes contexts (medical, legal).
• Ignoring background noise and audio quality upfront — STT accuracy degrades significantly with noisy or low-quality audio; addressing input audio quality (microphone choice, noise suppression) is often more impactful than switching STT providers.
• Treating streaming partial results as final — a streaming transcript's early partial output can change as more audio context arrives; don't treat an interim result as the confirmed final transcription.

Learn more

• Text to Speech for the inverse conversion.
• Accessibility Testing for verifying transcription-dependent features (like live captions) actually serve users who need them.