3 posts
What is speech recognition? Speech recognition (ASR, Automatic Speech Recognition) is the AI technology that lets a device or software automatically convert spoken audio into written text. This guide: a clear definition, how ASR works, its link to speech to text, models like Whisper, word error rate, call center analytics, KVKK, and FAQs.
Voice AI agents are far more than simple pipelines that convert speech to text and text back to speech. Real enterprise value emerges from the system’s ability to understand spoken input, manage natural dialogue flow, know when to speak and when to stay silent, and maintain responsiveness without interrupting users or creating awkward delays. A strong voice agent architecture therefore depends on the joint design of STT accuracy, TTS naturalness, turn-taking quality, barge-in handling, streaming infrastructure, latency budgets, context management, and safe action execution. This guide explains how to build production-grade Voice AI agents through the lenses of STT, TTS, conversational timing, latency design, architecture choices, evaluation metrics, enterprise use cases, and common design mistakes.
Speech-to-text systems convert human speech into text and power a wide range of enterprise applications, from call center analytics and meeting notes to voice assistants and accessibility solutions. Yet speech recognition is far more complex than it appears on the surface. Noise, accent, speaking rate, overlapping speech, punctuation, domain-specific jargon, numbers, dates, and multi-speaker structure all affect recognition quality. The shift from classical HMM-based pipelines to modern CTC, attention, RNN-T, and encoder-decoder architectures has also changed how ASR systems behave and how they should be evaluated. This guide explains how speech-to-text systems work, the major ASR architecture families, the most important error types, and how to measure quality properly in enterprise environments.