Speech-to-Text (STT)

Speech-to-text (STT), also known as automatic speech recognition (ASR), is the AI technology that converts spoken language into written text. Modern STT systems use deep learning models trained on vast amounts of audio data to achieve high accuracy across different accents, languages, and audio conditions. The technology has advanced dramatically in recent years. Models like OpenAI's Whisper have demonstrated near-human accuracy on many transcription benchmarks, handling background noise, multiple speakers, and varied recording quality with impressive reliability. Real-time STT is now standard in many consumer and enterprise applications. STT systems can provide simple transcription (converting speech to text) or more advanced features like speaker diarisation (identifying who said what), punctuation and formatting, timestamp alignment, and even translation (transcribing audio in one language to text in another).

STT is transforming how businesses capture and use spoken information. Meeting transcription allows teams to focus on discussion rather than note-taking, with AI producing accurate records that can be searched, summarised, and shared. Call centre analytics use STT to transcribe customer interactions for quality assurance, compliance monitoring, and insight extraction. Legal and healthcare sectors rely on STT for documentation — transcribing depositions, medical dictation, and patient consultations. Media companies use it for subtitling, content indexing, and accessibility compliance. The combination of STT with language models creates powerful workflows. Transcribed audio can be automatically summarised, key action items can be extracted, sentiment can be analysed, and insights can be generated — all from a simple audio recording. This turns previously ephemeral spoken information into structured, actionable data.