AI transcription
AI transcription gets the words. Not the meaning.
AI transcription — also called speech-to-text or automatic speech recognition (ASR) — uses neural networks to convert spoken audio into written words. Modern transcription models are accurate, fast, and cheap. But a transcript is a lossy compression of a conversation: it keeps what was said and discards how it was said.
What a transcript throws away
- Emotion
- Whether "great, thanks" is genuine gratitude or sarcasm reads identically in text. The voice tells them apart.
- Frustration
- Tension builds in pitch and pace long before it appears in word choice — invisible in a transcript.
- Intent
- A hesitant "I guess that works" and a confident one transcribe the same, but signal opposite outcomes.
- Emphasis & timing
- Pauses, stress, and speaking rate change what a sentence means. Text flattens all of it.
Transcription + understanding, together
AI transcription and speech understanding are complementary layers. Transcription answers “what words were spoken?”; understanding answers “what did the person actually mean?” Teams building voice agents, call analytics, or meeting tools typically run both: an ASR model for the verbatim text, and a speech understanding model like Oruk for emotionand speaking style.
The difference shows up in outcomes: an agent working from a transcript keeps reading its script while a caller escalates. An agent that also hears the frustration can de-escalate, reroute, or hand off — in real time, in the natural rhythm of speech.
Add understanding on top of your transcription stack
Oruk’s Resonance, Spectra 1, and Spectra 2 models can return the transcript together with emotion and speaking-style labels. Oruk publishes evaluation results on speech-emotion-bench . API v1 is an authenticated REST interface for English audio files.