What is speech recognition?
Speech recognition, also called automatic speech recognition (ASR) or speech-to-text, is the conversion of spoken audio into written text by software. The model takes an audio signal, breaks it into short frames, and predicts the sequence of words most likely to have produced it. Modern systems are neural networks trained on very large collections of transcribed speech, and the best of them handle accents, background noise, and domain vocabulary that defeated earlier systems. Several open-weight speech models are available and run well on a single GPU.
Related tasks are often bundled with ASR. Speaker diarization identifies who spoke when. Punctuation and formatting restore readable sentences. Timestamps align each word with the audio. Text-to-speech is the reverse process and pairs with ASR in conversational AI systems.
Why speech recognition matters
Speech is the largest untapped text source in most companies. Call center recordings, field technician voice notes, clinical dictation, meetings, and depositions all hold information that was never searchable because nobody transcribed it. ASR turns it into text that downstream natural language processing can classify, summarize, and search. It is also the entry point for voice interfaces, from a warehouse picker confirming a bin to a caller navigating a payment line.
Speech recognition in practice
A collections agency transcribes every outbound and inbound call. The transcripts feed a compliance check that flags required disclosures that were missed and language that regulators prohibit. Supervisors review flagged calls instead of random samples. The same transcripts give the agent assist tool the context to suggest the next step during a live call. Because the recordings contain account numbers and health information, the agency runs the speech model on its own servers rather than sending audio to an outside service.
In Kirk Tech Solutions engagements, call transcription is often the first speech workload a client brings in house, because the volume is steady and the content is regulated.
What to watch for
- Measure word error rate on your own recordings, with your telephony quality and your customers’ accents, rather than trusting published figures.
- Check how the system handles names, part numbers, and medication names, which carry most of the business value and most of the errors.
- Ask whether audio is retained by the provider and for how long; recordings are personal data under most privacy regimes.
- Plan for diarization if you need to know who said what, because basic ASR does not provide it.