{
  "id": 17989144,
  "title": "Building ultra-low-latency voice AI applications on AWS",
  "url": "https://urgent.news/2026/11/03/building-ultra-low-latency-voice-ai-applications-on-aws",
  "topic": "ai",
  "section": "AI",
  "published": "2026-11-03T16:40:54.000Z",
  "source": {
    "name": "AWS Machine Learning",
    "slug": "aws-machine-learning",
    "url": "https://aws.amazon.com/blogs/machine-learning/building-ultra-low-latency-voice-ai-applications-on-aws/"
  },
  "original_language": "en",
  "account": "Voice AI is rapidly reshaping human-computer interaction. Conversational AI applications like virtual assistants, contact centers, telehealth consultations, gaming, accessibility tools, and sales agents rely on near-instantaneous voice processing. However, delivering responsive, high-quality speech remains a technical challenge. Traditional transformer-based text-to-speech (TTS) models face a fundamental limitation: processing entire sequences before outputting any audio. This architecture creates a trade-off between quality and latency. A new class of models called State Space Models (SSMs) resolves this problem. SSMs process sequences step-by-step and maintain a compact internal state, avoiding the quadratic computational complexity of transformers. This efficiency allows SSMs to handle long audio sequences with minimal latency, making them ideal for real-time voice applications.\n\nCartesia, an AWS Partner specializing in real-time voice AI, leverages SSM technology to deliver sub-100ms text-to-speech and speech-to-text at global scale. Sonic, Cartesia's text-to-speech model, generates audio with almost instantaneous latency (first audio byte in ~90ms) and produces ultra-realistic speech in over 44 languages. Sonic supports streaming-native audio synthesis, context-aware prosody, and voice cloning from just 10 seconds of sample audio. Ink, Cartesia's speech-to-text model, offers streaming transcription with native turn detection, context-aware end-pointing, and multi-speaker handling. By deploying Cartesia on AWS, developers can build conversational AI applications that feel indistinguishable from human conversation, meeting the growing demand for real-time voice AI across industries.",
  "summary": "Learn how Cartesia, an AWS Partner, uses purpose-built State Space Models and AWS infrastructure to deliver sub-100ms text-to-speech and speech-to-text at global scale, and how to deploy its Sonic model on Amazon SageMaker AI or self-managed Amazon EC2 GPU instances.",
  "key_points": [
    "State Space Models (SSMs) enable sub-100ms voice AI processing",
    "Sonic text-to-speech model generates audio with ~90ms latency",
    "Ink speech-to-text model offers streaming transcription with multi-speaker handling"
  ],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}