● Covered by 1 source · 1 reportLow impact1 neutral
Cartesia uses State Space Models and AWS for sub-100ms voice AI applications
Key points
- Cartesia uses State Space Models (SSMs) for voice AI.
- Achieves sub-100ms text-to-speech and speech-to-text latency.
- SSMs process sequences differently than transformer models.
- AWS infrastructure supports global scaling of these applications.
Addressing Voice AI Latency
Conversational AI requires human-like responsiveness, but excess latency can disrupt conversational flow. Traditional transformer-based text-to-speech (TTS) models process entire sequences before producing output, leading to latency issues. This architectural limitation means higher quality often results in higher latency.
Cartesia's Approach with State Space Models
Cartesia, an AWS Partner, utilizes purpose-built State Space Models (SSMs) and AWS infrastructure to deliver sub-100ms text-to-speech and speech-to-text capabilities. SSMs offer a different approach to sequence processing compared to transformers, which typically compare every element in a sequence, leading to quadratic computational growth with longer sequences.
Impact on Conversational AI Applications
This low-latency capability supports various real-time voice AI applications across industries. Examples include AI agents in contact centers, remote patient consultations in telehealth, immersive experiences in gaming, accessibility tools like screen readers, and sales agents for lead qualification and customer interaction. The goal is to create voice experiences that are indistinguishable from human conversation.
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Reporting from
Cartesia, an AWS Partner, uses State Space Models (SSMs) and AWS infrastructure to achieve sub-100ms text-to-speech and speech-to-text latency for voice AI applications. This approach addresses the latency challenges of traditional transformer-based models, which process entire sequences before generating output. The integration allows for the development of real-time conversational AI experiences.