How AI Transcription Actually Works: A Behind-the-Scenes Look
Curious about the technology behind automatic speech recognition? We break down how modern AI models convert audio waves into accurate text, including speaker diarization.
From Sound Waves to Text
When you speak, you create vibrations in the air—sound waves. Converting these waves into written text is a remarkably complex task that humans do effortlessly but computers have only recently mastered.
The Evolution of Speech Recognition
Rule-Based Systems (1950s-1980s): Early systems tried to match sounds to predefined patterns. They worked for specific speakers saying specific phrases but failed in real-world conditions.
Hidden Markov Models (1990s-2000s): Statistical approaches improved accuracy significantly. These systems learned from data but still required careful acoustic modeling.
Deep Learning Revolution (2010s-Present): Neural networks changed everything. Models like Whisper and those used by ElevenLabs now approach human-level accuracy.
How Modern AI Hears
Today's speech recognition follows these steps:
1. Audio Preprocessing
The raw audio is converted into a spectrogram—a visual representation of frequencies over time. Think of it as a heat map showing which frequencies are active at each moment.
2. Feature Extraction
The AI identifies patterns in the spectrogram that correspond to phonemes, the smallest units of sound in language. English has about 44 phonemes.
3. Acoustic Modeling
Neural networks trained on thousands of hours of speech map acoustic features to likely phoneme sequences. These models learn accents, speaking styles, and audio conditions.
4. Language Modeling
The AI considers context to choose between similar-sounding words. "Write" and "right" sound identical, but the surrounding words help the model choose correctly.
5. Decoding
Finally, the system assembles the most likely sequence of words, balancing acoustic evidence with language probability.
Speaker Diarization: Who Said What?
Identifying individual speakers adds another layer:
- Voice Embedding: The AI creates a "fingerprint" of each voice's unique characteristics
- Clustering: Similar voice segments are grouped together
- Labeling: Each cluster is assigned a speaker label
This works even for voices the AI has never heard before, by comparing relative differences rather than matching known speakers.
Why Accuracy Varies
Several factors affect transcription quality:
- Audio quality: Background noise and echo degrade accuracy
- Accents: Models trained primarily on certain accents may struggle with others
- Technical vocabulary: Unusual words may be transcribed as common alternatives
- Speaking clarity: Mumbling or fast speech is harder to transcribe
The Future of Speech AI
The field continues advancing rapidly:
- Multilingual models handle code-switching between languages
- Real-time processing enables live captioning
- Emotion detection adds context beyond just words
- Custom vocabulary lets you train for your specific terms
At Sanekot AI, we integrate the best available models and continuously update as the technology improves.