Question 1

What is speech-to-text (STT)?

Accepted Answer

Speech-to-text, also called automatic speech recognition (ASR), is technology that converts spoken audio into written text. It's used for transcription, voice commands, captions, and accessibility. Modern STT uses deep learning to achieve 95%+ accuracy on clear audio.

Question 2

Which STT model is most accurate?

Accepted Answer

OpenAI's Whisper Large v3 is widely considered the most accurate multilingual STT model, with excellent handling of accents and noise. For English-only with real-time needs, Deepgram Nova 2 offers excellent accuracy with lower latency. AssemblyAI is best when you need additional features like speaker diarization.

Question 3

Does STT support multiple languages?

Accepted Answer

Yes! Modern STT models support many languages with automatic language detection. Whisper supports 99 languages, Deepgram supports 30+, and AssemblyAI supports 100+ languages. They can also translate speech from other languages into English text.

Question 4

What is speaker diarization?

Accepted Answer

Speaker diarization identifies different speakers in audio, labeling who said what. This is essential for meeting transcripts, interviews, and podcasts. AssemblyAI and Deepgram offer diarization built-in. With Whisper, you'd need to add a separate diarization model like pyannote.

Question 5

How accurate is real-time transcription?

Accepted Answer

Real-time STT typically achieves 90-95% accuracy on clear audio. Deepgram Nova 2 offers sub-300ms latency for real-time use. Accuracy can drop with background noise, overlapping speech, or heavy accents. Post-processing and custom vocabulary can improve results.

Question 6

Can I run STT models locally?

Accepted Answer

Yes! Whisper and Faster Whisper can run locally for privacy-sensitive applications. Faster Whisper uses CTranslate2 for 4x speedup and lower memory usage. For GPU inference, you can transcribe in real-time locally. CPU inference works for batch processing.

Question 7

How do I improve STT accuracy?

Accepted Answer

Improve accuracy by: 1) Using clean audio with minimal background noise, 2) Adding custom vocabulary for domain-specific terms, 3) Using a higher quality model tier, 4) Providing context about the audio content, 5) Using audio enhancement to clean up recordings first.