What is speech recognition (speech-to-text)?

Speech recognition: definition

Speech recognition translates audio into text, either live while speaking or afterward from a recording. It is the basis of dictation functions, automatic minutes, subtitles and voice assistants. It differs from speech synthesis, which goes the other way and outputs text as a voice.

Earlier systems had to be trained for individual speakers. Today’s models learn from very large amounts of speech data and cope with different voices, languages and devices. One well-known example is the open-source model Whisper; there are also services from the major cloud providers.

How speech-to-text works

The audio signal is split into short segments and converted into a sound profile. A neural network maps this profile to sounds, words and finally sentences, using knowledge of the language to choose the right word among similar-sounding ones. Many systems also add punctuation and capitalization.

Quality depends on the recording and the environment: microphone, background noise, dialect and technical vocabulary all play a role. Good solutions allow a custom dictionary for product and company names and can separate speakers on request, which matters for meeting minutes. Recording in a quiet environment with a good microphone often improves the result more than any software setting.

Real-world example: site report by voice message

A trades business has its installers speak their daily report into a smartphone on the job site. Speech recognition converts the recording into text, and a language model organizes it into work steps, materials used and anything unusual. The result appears as a draft in the job management system. In addition, photos of the site can be attached directly to the report.

The installer briefly reviews the draft and approves it. Evening paperwork disappears, documentation becomes more complete, and change orders are easier to substantiate. A dictionary with components and manufacturer names was important so that these are spelled correctly. After a short introduction, the installers used the function on their own because it was faster than typing.

Data protection, limits and imple­menta­tion

Voice recordings often contain personal data. Conversations may only be recorded or transcribed with the knowledge and consent of those involved; with cloud services, data processing agreements and server location belong on the checklist. With noise, dialect or people talking over each other, the error rate stays higher, so important texts should be checked.

Nano Concepts integrates speech recognition into apps and workflows, from the dictation function to the phone assistant, and selects services by quality and data protection. We test with recordings from your daily work to see how well dialects, technical terms and background noise are recognized, and clarify whether processing should run locally or in the cloud.

• Define the purpose and audio quality (microphone, environment)
• Use a custom dictionary for technical and product terms
• Ensure consent and information for conversation partners
• Clarify server location, retention period and data processing agreement
• Check important transcripts before further use