Overview
Speech-to-text (ASR) converts an audio waveform into words. Contemporary systems are trained on very large multilingual audio sets, handle accents and background noise far better than older ones, and add diarisation so a transcript says who spoke.
How it works
Audio is converted to a spectrogram, encoded, and decoded into text by a sequence model. Separate stages add punctuation, casing, timestamps and speaker diarisation. Streaming models emit partial results with sub-second latency.
Supported AI tools
Support level is recorded per tool, so an integration is never shown as a built-in feature.
Use cases
Meeting transcription
Produce a searchable record with speaker labels and timestamps.
CorporateCall-centre analytics
Transcribe calls so quality and compliance can be measured at scale.
SupportVideo captions
Generate accurate captions for accessibility and silent autoplay.
MediaClinical dictation
Let practitioners dictate notes straight into the record system.
HealthcareBenefits
- Makes meetings and calls searchable after the fact.
- Produces captions that satisfy accessibility requirements.
- Feeds downstream summarisation and analytics.
- Enables voice interfaces and real-time dictation.
Limitations
- Heavy accents, crosstalk and poor microphones cut accuracy.
- Domain jargon and proper nouns need a custom vocabulary.
- Speaker separation degrades once several people talk at once.
- Streaming accuracy trails batch accuracy noticeably.
What to look for when choosing a tool
- Word error rate on audio like yours, not a demo clip
- Custom vocabulary for names and jargon
- Speaker diarisation and timestamp granularity
- Streaming latency if you need live output
- On-device or private deployment for confidential audio