What is speech-to-text (STT)?

Speech-to-text (STT) is technology that converts spoken words into written text. You might also see it called speech recognition, voice-to-text, or automatic speech recognition (ASR). If you have ever used Siri, dictated a text message, or seen live captions on a video call, you have used speech-to-text.

For businesses, STT is the technology behind call transcriptions, AI phone systems, and any tool that needs to understand what someone is saying on the phone.

How it works in plain terms

Think of STT as the “ears” of an AI system. When a caller speaks, the sound travels through the phone line as audio. STT software analyzes that audio — the specific sounds, patterns, and rhythms — and matches them against its understanding of language to produce written text.

Modern STT does not just match sounds to words one at a time. It considers the full context of a sentence. If someone says “I need a new sod installation,” the system uses context to recognize “sod” rather than guessing a similar-sounding word. It understands that “sod installation” is a common phrase in a way that “sawed installation” is not.

This all happens in real time. As your caller speaks, the words are being converted to text nearly instantly — fast enough to power a live AI conversation without awkward pauses.

How accurate is it today

Modern speech-to-text systems achieve 95% or higher accuracy under good conditions. That means in a 100-word conversation, 95 or more words will be transcribed correctly. For clear speakers on a good phone connection, accuracy can reach 98-99%.

To put that in perspective, that is roughly the same accuracy rate as a human transcriptionist. In some controlled tests, AI speech recognition actually outperforms humans.

But “good conditions” is the key phrase. Accuracy drops when the system faces real-world challenges.

Where it still struggles

Speech-to-text is not perfect, and knowing where it stumbles helps you set realistic expectations:

Background noise. If your caller is standing next to a running leaf blower, mower, or pressure washer, the system has a harder time picking out their words from the noise. Phone audio quality matters too — a clear cell signal produces better results than a choppy one.

Accents and dialects. STT systems are trained primarily on standard American English. Regional accents, non-native speakers, and heavy dialects can reduce accuracy. The technology has improved significantly here, but it is still not as good as a patient human listener.

Industry jargon. Terms that are common in field service — “dethatching,” “pre-emergent,” “hardscaping,” “French drain” — may not be in a general-purpose STT system’s vocabulary. Better systems can be trained on industry-specific terminology, which makes a real difference.

Crosstalk and interruptions. If multiple people are talking at once, or a caller keeps interrupting themselves, the system may struggle to separate and accurately transcribe the overlapping speech.

Mumbling and trailing off. We all do it. “Yeah, I need, uh… the thing where you, you know, come out and do the…” A human can usually fill in the gaps. STT systems are getting better at this but still miss more than a human would.

Practical applications for field service

STT shows up in several tools field service businesses already use or will encounter:

• Call transcriptions. Every call automatically converted to text you can search, review, and store. Useful for training, dispute resolution, and remembering what a customer actually asked for.
• AI phone systems. STT is the first step in any AI voice agent — it needs to convert speech to text before it can understand the caller and respond.
• Voicemail transcription. Instead of listening to a two-minute voicemail, you read a text summary and decide if it needs an immediate callback.
• Voice search and commands. “Show me tomorrow’s schedule” spoken into your field service app instead of tapping through menus.

The bottom line

Speech-to-text is foundational technology — not flashy on its own, but essential to everything that makes AI phone systems work. The accuracy is good enough for real business use today, with the understanding that noisy environments and unusual speech patterns can still cause the occasional hiccup. For most routine business calls, it gets the job done reliably.