Speech to Text Explained: How It Works, Accuracy and Use Cases

Speech to text is the technology that listens to spoken language and writes it down as text. Modern systems do this with neural networks trained on hundreds of thousands of hours of audio, and on a clear recording they produce a usable transcript in a fraction of the time a person would need. This article explains, without the maths, how that happens, why accuracy varies so much between recordings, and where the technology is actually used.

A short history, in three steps

Speech recognition is older than most people think. In the 1950s, Bell Labs built a machine that could recognise spoken digits from a single speaker. For decades afterwards, progress was slow: systems needed to be trained on each user's voice, worked with small vocabularies and demanded careful, isolated words.

The 1980s and 1990s brought statistical methods, in particular hidden Markov models, which allowed continuous speech and larger vocabularies. Dictation software like Dragon NaturallySpeaking became commercially viable, though it still needed training sessions and a quiet room.

The breakthrough came around 2012 to 2016, when deep neural networks replaced the older acoustic models, and again around 2020 to 2023, when large end-to-end models trained on huge, diverse datasets (OpenAI's Whisper being the best-known open example) made robust transcription available to anyone with a laptop or a browser. Today's Audiotexta, like most modern transcription services, is built on this generation of models.

How speech to text works, step by step

Under the hood, a modern speech-to-text system runs a pipeline that looks roughly like this.

1. Capturing and cleaning the audio

Audio arrives as a waveform: thousands of amplitude samples per second. The system resamples it to a standard rate (often 16 kHz, enough for speech), converts stereo to mono, and may apply light noise suppression. Nothing clever yet, but this stage already determines a lot: a muffled phone recording loses high-frequency detail that no model can recover.

2. Turning sound into features

The waveform is cut into short frames, around 20 to 25 milliseconds each, and each frame is converted into a compact description of which frequencies are present. The most common representation is a log-mel spectrogram, a picture of frequency over time that roughly mirrors how the human ear perceives pitch. The model sees this spectrogram, not the raw audio.

3. The acoustic model

A neural network reads the sequence of frames and predicts, for each small slice of time, which sounds (or directly which characters or sub-word tokens) are being spoken. Older systems predicted phonemes and needed a separate pronunciation dictionary; modern end-to-end models, usually transformer-based, go straight from spectrogram to text tokens.

4. The language model

Sounds alone are ambiguous: "recognise speech" and "wreck a nice beach" are acoustically close. A language model, trained on text, scores which word sequences are plausible. In end-to-end systems this knowledge is baked into the same network, which is why they handle context better than their predecessors and why they occasionally "hallucinate" a fluent sentence when the audio is actually silence or noise.

5. Decoding and post-processing

The decoder searches for the most likely text given both acoustic and linguistic evidence. Afterwards, post-processing adds punctuation and capitalisation, formats numbers and dates, and attaches timestamps to words. Separate models handle speaker diarization (who spoke when) and, if requested, translation or summarisation.

What "accuracy" actually measures

The standard metric is word error rate (WER): the number of substitutions, deletions and insertions needed to turn the machine transcript into a correct human one, divided by the number of words in the reference. A WER of 5% means roughly one word in twenty is wrong.

A few things to keep in mind when you see accuracy claims:

• Benchmarks are clean. Published numbers usually come from datasets such as LibriSpeech (audiobooks read by volunteers) which are far easier than a conference call.
• WER treats all errors equally. Missing "the" and misspelling a client's name both count as one error, but only one of them matters to you.
• Punctuation and casing are often excluded from the metric, yet they make the difference between a readable transcript and a wall of text.
• Language matters more than vendor. The same model that scores a low WER in English may do noticeably worse in Polish or Finnish simply because there was less training data.

In practice, a transcript with a WER around 5 to 10% on a good recording needs a quick review; one at 20 to 30% from a noisy room needs serious editing and may be faster to retype in places. Our guide on getting accurate transcripts covers the practical levers you control.

Why some recordings transcribe badly

Models fail in predictable ways. If you know the patterns, you can prevent most of them before recording.

• Distance. Every metre between mouth and microphone adds room echo and background noise.
• Overlapping speech. Two people talking at once produce a signal no current system separates cleanly.
• Domain vocabulary. Medical, legal and technical terms appear rarely in training data, so the model substitutes a common word that sounds similar.
• Code-switching. Sentences that mix two languages confuse systems that expect one; some models handle it, many do not.
• Non-speech audio. Music, laughter and long silences can trigger hallucinated text in end-to-end models.
• Very short clips. A two-second voice command gives the model little context to work with.

Cloud, on-device or hybrid?

Where the model runs shapes privacy, cost and capability.

On-device speech to text, such as the dictation built into iOS, Android, Windows and macOS, keeps audio on your hardware. It is free, works offline and is good enough for short notes, but models are smaller, languages fewer and there is no editor or speaker labelling.

Cloud services run larger models on GPUs. They support more languages, handle hour-long files, and add diarization, summaries, translation and team sharing. The cost is that your audio leaves your device, which raises data protection questions; EU users in particular should look at where processing happens, how long files are kept and whether they are used for training. Audiotexta deletes files after the retention period you choose and does not use your content to train models.

Open-source models run locally, such as Whisper through whisper.cpp or faster-whisper, sit in between: large-model quality, nothing uploaded, but you provide the hardware and do without the conveniences. Our comparison of Whisper and cloud speech APIs goes into the trade-offs in more detail.

Where speech to text is used today

The technology is embedded in more places than most people notice.

• Transcription services for interviews, meetings, lectures and podcasts, where the output is a document you edit and share.
• Captions and subtitles for video platforms, broadcasters and online courses, where timing matters as much as the words.
• Voice assistants and dictation on phones and in cars, where latency is critical and the vocabulary is narrow.
• Call centres that transcribe calls for quality monitoring, compliance and analytics.
• Accessibility, from live captions in video calls to real-time transcription apps for deaf and hard-of-hearing users.
• Healthcare and legal documentation, where dictation replaces typing but a human still signs off on the result.
• Search and media monitoring, which index spoken content so that it can be found like text.

If your use case is one of the first two, the practical next step is simple: upload a file to a transcription tool and see what comes back. Our guide on how to transcribe audio to text walks through the process, and the Audiotexta app lets you try it with a free plan before deciding whether automatic transcription fits your workflow.

What is changing right now

Three trends are worth watching if you rely on speech to text for work.

First, multilingual models are closing the gap between English and other languages, and increasingly handle mixed-language speech without being told which language to expect. Second, speaker diarization is improving quickly; the days of "Speaker 1" being three different people are not over, but the errors are rarer. Third, transcription is becoming an input rather than an output: once the text exists, the same systems summarise it, extract action items, translate it and generate chapters, so the transcript itself is read less often than the summary built from it.

The honest caveat is that none of this removes the need to check. A model that is right nineteen times out of twenty is impressive and, for anything you publish or act on, still worth a few minutes of review with the audio playing alongside.

Quick glossary

• ASR: automatic speech recognition, the formal name for speech to text.
• WER: word error rate, the standard accuracy metric; lower is better.
• Diarization: splitting audio by speaker and labelling who said what.
• Spectrogram: a frequency-over-time image of audio that models take as input.
• End-to-end model: a single neural network that maps audio to text without separate acoustic, pronunciation and language components.
• Hallucination: fluent text generated where the audio contains no matching speech, usually in silence or music.
• Timestamps: start and end times attached to words or segments, used for navigation and subtitles.
• Custom vocabulary: a list of names or terms supplied to a model to improve recognition of rare words.