Daily Dose of Data Science | Avi Chawla | Substack

Real-time voice interactions are becoming increasingly popular. This post provides a detailed, step-by-step guide on building a real-time Voice RAG Agent. Key components include using AssemblyAI for speech-to-text transcription, LlamaIndex for document-based answers, and Cartesia for generating seamless speech. The post includes a video and open-source code for easy implementation.

Building a Real-time Voice RAG Agent

Deepgram

Nova-3 Medical, the latest from Deepgram, offers best-in-class medical speech-to-text capabilities. The model provides unmatched accuracy in healthcare settings, capturing vital details like medication names and diagnostic terms while filtering out irrelevant noise. It is HIPAA-compliant and supports flexible customization with Keyterm Prompting. Nova-3 Medical outperforms its competitors significantly in terms of Word Error Rate (WER) and Keyterm Error Rate (KER). It integrates seamlessly with existing healthcare infrastructures and is optimized for performance and scale.

Introducing Nova-3 Medical: The Future of AI-Powered Medical Transcription

Speech recognition is a technology that enables computers to interpret and transcribe spoken language into text or commands. It involves converting audio signals into digital data, analyzing and processing speech patterns, and generating textual representations using machine learning and natural language processing algorithms. Readers can explore speech recognition techniques, models, and applications in various domains, such as virtual assistants, voice-enabled interfaces, and transcription services, understanding its capabilities and limitations in real-world scenarios.

Best of Speech Recognition — March 2025