Speech-to-Text
Definition
AI systems that convert spoken language into written text, also known as automatic speech recognition (ASR). Modern approaches use neural networks trained on massive audio-text datasets.
Why It Matters
Speech-to-text (STT) is the entry point for voice-based AI applications. Whether you’re building transcription services, voice assistants, meeting summarizers, or accessibility tools, reliable speech recognition is the foundation.
The field was transformed by Whisper (OpenAI), which achieved near-human accuracy across many languages and conditions. This raised the bar for what’s possible and made high-quality STT accessible through both API and open-source deployment.
For AI engineers, STT is often a component in larger systems: transcribe audio, process text with an LLM, generate response. Understanding the capabilities and limitations of STT helps you build robust end-to-end voice applications.
Implementation Basics
How Modern STT Works
- Audio preprocessing: Convert to spectrograms or mel features
- Encoder: Process audio features (transformer or CNN)
- Decoder: Generate text tokens (autoregressive)
- Post-processing: Punctuation, capitalization, formatting
Popular Models
- Whisper (OpenAI): Best open model, multilingual, robust
- wav2vec 2.0: Self-supervised, good for fine-tuning
- Conformer: Google’s architecture for production
- AssemblyAI, Deepgram: Commercial APIs
Whisper Model Sizes
| Model | Parameters | English WER |
|---|---|---|
| tiny | 39M | ~13% |
| base | 74M | ~9% |
| small | 244M | ~6% |
| medium | 769M | ~5% |
| large | 1.5B | ~4% |
Key Capabilities
- Language detection (automatic or specified)
- Timestamp generation (word or segment level)
- Speaker diarization (who said what)
- Translation (speech in language A → text in language B)
- Punctuation and formatting
Practical Considerations
- Latency: Real-time vs. batch tradeoffs
- Accuracy: Depends on audio quality, accents, domain
- Cost: API pricing vs. self-hosted compute
- Privacy: Local processing for sensitive audio
Audio Quality Factors
- Noise level significantly impacts accuracy
- Multiple speakers are harder than single speaker
- Domain-specific vocabulary may need adaptation
- Phone audio quality vs. professional recording
Integration Patterns
# Whisper example
import whisper
model = whisper.load_model("base")
result = model.transcribe("audio.mp3")
print(result["text"])API Options
- OpenAI Whisper API
- AssemblyAI (real-time streaming)
- Google Cloud Speech-to-Text
- AWS Transcribe
- Azure Speech Services
Common Use Cases
- Meeting transcription and summarization
- Podcast and video subtitles
- Voice search and commands
- Call center analytics
- Medical dictation
- Accessibility services
Challenges
- Background noise and reverb
- Heavy accents and dialects
- Technical jargon and proper nouns
- Code-switching (mixing languages)
- Real-time latency requirements
Source
Whisper is a multitask speech model trained on 680,000 hours of multilingual audio, demonstrating robust speech recognition approaching human-level accuracy across diverse domains.
https://arxiv.org/abs/2212.04356