Speech-to-Text (STT) (تحويل الكلام إلى نص)
Speech-to-Text (STT) is technology that converts spoken words into written text in real time. It uses acoustic and language models to recognize sounds and map them to words, powering transcription, subtitles, voice assistants, and the listening layer of AI call centers.
In Iraqi dialect
يحول الحچي إلى نص مكتوب
In detail
STT is the ears of any voice system. When audio arrives, the engine breaks it into tiny frames, matches acoustic patterns to phonemes, and uses a language model to pick the most probable words — handling accents, background noise, and dialects along the way. Accuracy is measured by word error rate and has improved dramatically with deep learning. In call centers, STT transcribes every conversation so AI can understand it, agents can see live transcripts, and quality teams can search recordings. Combined with diarization it labels who spoke when, making transcripts genuinely useful for analytics.
Practical example
Call-center software transcribes each call with STT so supervisors can search for complaints about delivery delays.
Frequently asked questions
How accurate is speech-to-text today?
Modern engines reach 90-95%+ accuracy on clear audio, and keep improving for Arabic dialects and noisy calls.
What is the difference between STT and transcription?
STT is the underlying technology; transcription is the practical output — a written record of spoken audio.
Related terms
Related services
AI Transcription Service
Fast, accurate speech-to-text transcription for meetings, interviews, podcasts, and legal recordings.
AI Voice Agent for Call Centers
Human-like AI voice agents that handle inbound and outbound calls at scale.
AI Meeting Summarizer
An AI agent that joins meetings, transcribes conversations, and generates action-ready summaries.