CyberFreezeDev
Loading
Converting recorded or live speech into text that software can search and act on.
It writes down what was said, often with punctuation, timestamps and speaker labels. Batch mode handles finished files; streaming returns text while people speak.
Speech-to-text, also called automatic speech recognition (ASR), is software that listens to audio and writes down what was said. Good systems add punctuation, timestamps for each word or segment, and diarisation, which labels who spoke when ("Speaker 1", "Speaker 2").
It comes in two modes. Batch transcription processes a finished file such as a call recording or a WhatsApp voice note. Streaming transcription returns text while the person is still speaking, which voice agents need to reply quickly.
A neural network reads audio as a picture of frequencies and predicts the likeliest words. Accuracy is measured as word error rate.
Audio is cut into short frames and converted into a spectrogram, a picture of which frequencies are present over time. A neural network, typically an encoder that reads the audio and a decoder that writes tokens of text, predicts the most likely words. OpenAI's Whisper is a well-known open model of this kind and can run on your own hardware. Commercial APIs from Google, Microsoft, AWS, Deepgram and others offer the same job as a hosted service.
Accuracy is usually reported as word error rate (WER): substitutions, deletions and insertions divided by the number of words actually spoken. Lower is better.
Call transcripts become searchable, and an LLM writes summaries and next steps into the CRM.
Transcripts make calls searchable and let an LLM write the summary and next steps into the CRM.
Transcribing voice notes lets one automation handle both voice and text messages.
Field staff and customers send audio. Transcribing it lets the same automation handle voice and text messages.
Dictated notes become draft records that a doctor reviews and signs.
Dictated notes become draft records that a doctor reviews and signs.
Searchable transcripts and subtitles for recorded meetings and training sessions.
Searchable transcripts and subtitles for recorded sessions.
Tamil and English sales calls are transcribed, an LLM fills four CRM fields, and a rep checks them before saving.
A sales team in Chennai records calls in a mix of Tamil and English. Each recording is sent to a speech-to-text model, the transcript goes to a large language model with a short prompt, and the model fills four CRM fields: product interest, budget mentioned, objection and next follow-up date. A rep checks the fields before saving. The audio and transcript are kept for the period the company's retention policy allows, then deleted.
Mixed languages, accents, phone lines and noise raise errors. Test on your own recordings.
Code-switching (Tamil and English, Hindi and English), accents, phone-line compression and background noise all raise the error rate. Test on your own recordings.
Recording calls involves personal data. Tell callers and follow the DPDP Act or UK GDPR.
Recording and transcribing calls involves personal data. Tell callers, and follow the DPDP Act 2023 in India or UK GDPR in the UK.
Check phone numbers, amounts and codes against your records or with a person.
Phone numbers, amounts and product codes are where errors cost money. Validate them against your records or ask a person to confirm.
Yes, with varying quality. Whisper and the major cloud APIs support Hindi, Tamil and other Indian languages, and open projects from AI4Bharat focus on Indian languages specifically. Quality differs a lot by language and audio quality, so run a test on a sample of your own calls before choosing.
Yes. Open models such as Whisper run on a local GPU or even a CPU for small volumes, so audio never leaves your network.
Diarisation splits a recording by speaker, so the transcript shows who said each line. It matters for calls and meetings where you need to know whether the customer or the agent said something.
Last updated