Speech-to-Text

August 27, 2026
|By Julia Schönau

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Speech-to-text, or SST for short, refers to the automatic conversion of spoken language into machine-readable text. This process forms the technical foundation for all voice-based systems designed to respond to human voice input, including voicebots, voice assistants, and automatic transcription services. The processing is carried out by artificial intelligence models that analyze audio data and generate a text representation from it.

Technical Foundation: From Cascade Models to End-to-End Systems

Earlier approaches to speech recognition were based on so-called cascade models, in which multiple specialized components were processed sequentially. First, the audio signal was analyzed acoustically; then, speech units were recognized; and finally, these were converted into text. Each of these stages could introduce errors that propagated through the pipeline.

Modern SST systems, on the other hand, rely on end-to-end (E2E) models that map the entire process within a single neural network. Speech is no longer processed in separate steps but is converted directly from the raw audio signal into text. This reduces the error rate and shortens latency, which is particularly important for real-time applications such as voicebots.

Speech Recognition and Natural Language Processing

SST is typically the first step in a larger processing chain. The recognized text input is then forwarded to a natural language processing system, which interprets the content and deduces the speaker’s intent. Without reliable speech recognition up front, the downstream processing cannot deliver accurate results.

In addition to simple word recognition, modern SST systems automatically add punctuation and context-sensitive corrections to the transcribed text. This results in a readable, structured text that can be processed directly without requiring manual editing.

A Comparison of Cloud Solutions and Open-Source Systems

Companies are often faced with a choice between cloud APIs such as Google Cloud, AWS Transcribe, or specialized providers like AssemblyAI, and open-source solutions such as OpenAI Whisper or DeepSpeech. While cloud solutions stand out for their immediate availability and extensive language support (over 100 languages), open-source engines offer full control over data. Speech-to-text (SST) supports both approaches and helps minimize infrastructure costs (Total Cost of Ownership) through efficient GPU utilization and CUDA acceleration.

Frequently Asked Questions About Speech-to-Text (SST)

Speech recognition, also known as Automatic Speech Recognition, is the core technology that converts audio signals into sequences of words. Speech-to-text describes the complete process that goes beyond this: It includes automatic punctuation, context-sensitive error correction, and the preparation of the text for further processing. SST systems therefore do not deliver a raw string of words, but rather a structured, readable text.

Modern SST models can be trained to recognize specific technical terms from particular industries—such as the medical or legal fields—using what is known as “custom vocabulary.” Accents and dialects are generally processed reliably by current end-to-end (E2E) models, provided the model has been trained with appropriate data. The recognition quality depends heavily on the model used and the acoustic quality of the input signal.

When using cloud-based SST services, audio data is transmitted to external servers. For companies with strict data protection requirements—such as those in the healthcare sector or public administration—on-premises solutions or locally operated open-source models offer an alternative. In these cases, the audio material remains entirely within the company’s own infrastructure and does not come into contact with any external systems.

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