Definition
Speech-to-text (STT), also called automatic speech recognition (ASR), is a technology that converts spoken language into written text. It is the first step in any voice AI system, allowing machines to "hear" and process what a person is saying.
Modern STT systems use deep learning models trained on massive datasets of human speech. These models can handle diverse accents, dialects, background noise, and speaking speeds with high accuracy. They process audio in real time, converting speech to text with minimal delay, which is essential for conversational applications where response speed matters.
The accuracy of STT has improved dramatically in recent years. Current state-of-the-art systems achieve word error rates below 5% for clear speech in English, approaching human-level transcription accuracy. They can also handle domain-specific vocabulary when fine-tuned for particular industries like healthcare, legal, or real estate.
In the context of voice AI for business, STT serves as the input layer. When a customer calls and says, "I need to schedule an appointment for my dog's annual checkup," the STT system transcribes that speech into text. The text is then processed by the AI's natural language understanding system to determine intent and take action.
STT also enables call transcription and analytics. Businesses can automatically transcribe every phone call, creating searchable records of customer interactions. These transcriptions can be analyzed for trends, quality assurance, and training purposes.
Why It Matters for Business
Accurate STT is the foundation of reliable voice AI. If the system misunderstands what a caller says, everything downstream fails. High-quality STT means fewer miscommunications, less caller frustration, and more successful automated interactions. Beyond real-time voice AI, STT also enables businesses to mine their call recordings for insights, track common customer questions, and improve service quality.
Real-World Example
A multi-location veterinary practice uses STT-powered call transcription across all their locations. Every call is automatically transcribed and tagged by topic (appointment, prescription refill, emergency, billing). Management uses this data to identify the most common reasons for calls, discover training opportunities for staff, and track how well their AI phone system handles different types of inquiries.