Machine Translation (MT)
The use of software to automatically translate text or speech from one language to another without human involvement.
Understanding Machine Translation (MT)
Machine translation (MT) has evolved dramatically from rule-based systems in the 1950s to today's neural machine translation (NMT) models. Modern NMT uses deep learning with transformer architectures (the same technology behind GPT and other AI models) to produce translations that capture context, idiom, and nuance far better than previous approaches. Machine translation is used in both real-time (live speech translation) and batch (document translation) scenarios. While not yet matching the best human translators for literary or highly nuanced content, modern MT is excellent for conversational, religious, educational, and business contexts.
How Selah Translate Uses Machine Translation (MT)
Selah Translate uses advanced neural machine translation models to convert transcribed text from the source language to the target language in real time. The system is optimized for the types of content most commonly translated in live settings — sermons, lectures, presentations, and conversational speech — delivering accurate, natural-sounding translations with context-aware processing.
Related Terms
Real-Time Translation
The instant conversion of spoken or written language from one language to another as it occurs, with minimal perceivable delay.
Neural Machine Translation (NMT)
A machine translation approach using deep neural networks to learn translation patterns from large bilingual datasets, producing more natural and contextually accurate translations than traditional methods.
Speech-to-Text (STT)
Technology that converts spoken language into written text in real time, also known as automatic speech recognition (ASR).