Revolutionize your call operation with Retell
Call transcription turns phone conversations into written text. See how it works, real-time vs post-call transcription, accuracy, legal consent and business use cases.
Call transcription is the process of converting a phone conversation into written text, either live during the call or after it ends, usually with automatic speech recognition (ASR) software that labels who said what. It uses speech-to-text technology so voice conversations can be searched, analyzed and acted on across business systems.
In AI-driven systems, call transcription happens automatically and in real time, enabling not just record-keeping, but also fueling advanced services like intent recognition, sentiment analysis, compliance tracking, and training data generation.
For businesses adopting AI voice agents, transcription forms the connective tissue between spoken language and business action.
High-quality call transcription helps teams:
Unlock deeper call analytics, surfacing trends and operational insights.
Enable real-time decision-making, such as triggering workflows based on keywords.
Support compliance by creating auditable conversation records.
Fuel AI learning by building better training datasets for fine-tuning models.
Real-time transcription converts speech to text while the call is happening. AI voice agents depend on it to understand callers, and it powers live agent assist and keyword alerts.
Post-call transcription processes the recording after the call ends. It is used for quality review, compliance records, call summaries and analytics.
Automated transcription uses ASR software and costs a fraction of a cent to a few cents per minute. Human transcription is slower and more expensive, and is mostly used where near-perfect accuracy is required, such as legal records.
Automatic Speech Recognition (ASR)
Transcribes live caller speech into text during the conversation.
Speaker separation (diarization)
The system labels each line by speaker, such as caller and agent, so the transcript reads like a conversation.
Large language models (LLMs)
Structure the text for analysis by tagging intent, sentiment and action items.
Storage and Retrieval
Transcripts are logged in CRMs, ticketing systems, or databases, tied to customer profiles or case records.
Optional Summarization
Advanced AI systems can auto-generate conversation summaries to make transcripts easier to review.
Redaction
Sensitive details such as card numbers or health information can be removed from the transcript before it is stored.
Accuracy depends on audio quality, background noise, accents, crosstalk and industry terms. Accuracy is usually measured as word error rate (WER), the share of words transcribed wrongly. Phone audio is lower quality than a recording made on a good microphone, so test any tool on your own calls, and add custom vocabulary such as product names where the tool allows it.
Recording and transcribing calls is legal in the US when you follow consent rules. Federal law and most states require one party's consent, while some states, including California, Florida, Illinois, Maryland, Massachusetts, Montana, New Hampshire, Pennsylvania and Washington, require everyone on the call to consent. Most businesses play a notice at the start of the call ("This call may be recorded and transcribed"). This is not legal advice; check the rules for where your callers are.
In Retell AI, every call produces a full transcript and a timestamped, speaker-labeled version you can pull through the API after the call. Post-call analysis then summarizes the call, scores sentiment and extracts the fields you define, such as claim type or callback number, and can push them into your CRM.
With accurate transcription, every conversation becomes searchable data for quality review, compliance and training.
A call recording is the audio file. Call transcription turns that audio into text you can search, analyze and store in other systems.
Yes. Automatic speech recognition transcribes calls in real time or after the call, and AI can then summarize the transcript and pull out key details.
Automated transcription is usually billed per minute of audio, often a fraction of a cent to a few cents per minute. Human transcription costs much more. On Retell AI, speech-to-text is included in the per-minute call price.