Speech-to-text (STT), also called automatic speech recognition (ASR), is the technology that transcribes spoken language into written text in real time. Modern STT systems use deep learning models trained on massive amounts of speech data to achieve high accuracy across different accents, speaking speeds, and audio conditions. In business applications, STT is the first step in processing a phone call: the caller speaks, the audio is transcribed, and then NLP processes the text to understand the meaning. Accuracy and latency are the key performance metrics for STT in voice AI applications, as errors in transcription cascade into misunderstandings.
The quality of speech-to-text directly affects how well an AI receptionist performs. Poor transcription leads to misunderstood requests, missed details, and caller frustration. For home service businesses where accurate job information (address, problem description, urgency) is critical, high-accuracy STT is non-negotiable.
AutoRev uses enterprise-grade speech-to-text technology optimized for phone call audio quality. Call transcripts are stored with each call record, giving you a written record of every customer interaction for training, quality review, and dispute resolution.
Technology that converts written text into spoken audio, enabling AI systems to speak responses out loud in natural-sounding voices.
Artificial intelligence that processes spoken language, enabling machines to listen to, understand, and respond to human speech in real time.
A branch of artificial intelligence that enables computers to read, understand, and interpret human language as it is naturally spoken or written.
Technology that enables computers to understand and respond to human language in a natural, dialogue-based way, powering voice assistants, chatbots, and AI receptionists.