AI transcription
AI transcription gets the words. Not how they were said.
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.
- Hesitation & confidence
- A hesitant "I guess that works" and a confident one transcribe the same, but sound very different.
- Emphasis & timing
- Pauses, stress, and speaking rate change how the same words are delivered. Text flattens all of it.
Transcription + understanding, together
AI transcription and speech understanding are complementary layers. Transcription answers “what words were spoken?”; understanding answers “how were those words said?” Teams evaluating prerecorded voice-agent calls, completed customer-service calls, or recorded meetings can run both: an ASR model for the verbatim text and an acoustic model like oruk for emotionand speaking style.
The difference shows up in outcomes: a review pipeline working from transcripts alone misses the calls where tone contradicted the words. One that also scores frustration and hesitation can prioritize those recordings for human attention.
Scope note: oruk API v1 is file-based — it analyzes prerecorded audio, not live streams — and it does not infer intent. See the full capabilities and scope statement.
Add understanding on top of your transcription stack
oruk’s Resonance and Spectra 1 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.