Idea
Real-time spoken AI that thinks while listening to reduce latency and improve interaction accuracy.
Research Paper
Core Innovation
This paper introduces SHANKS, a framework that streams input speech in chunks and generates unspoken chain-of-thought reasoning concurrently with user speech. Unlike prior models that think only after full input, SHANKS enables interruption and tool calls mid-turn, significantly improving interaction speed and accuracy in spoken language tasks.
Why It Matters
Current spoken language models wait until users finish speaking before processing, causing delays and poor interaction quality. SHANKS enables continuous reasoning during speech, allowing timely interruptions and faster task completion, which is critical for real-time applications like tutoring and voice assistants. This approach can transform conversational AI workflows by making interactions more natural and efficient at scale.
Market Size (TAM)
$10–20B TAM for conversational AI and voice assistants; $2–5B SAM from EdTech, customer support, and healthcare sectors. Driven by demand for real-time, low-latency voice interaction and AI-assisted tutoring.
Potential Customers & Pain Points
- Voice assistant providers–High response latency and poor real-time interaction
- EdTech platforms–Need to detect and correct user errors promptly
- Customer support centers–Require faster issue resolution during calls
- Healthcare–Need real-time monitoring and intervention during patient conversations.
Business Model
Licensing SHANKS technology as an API or SDK to voice assistant developers, EdTech companies, and enterprise customer support platforms; offering customization and integration services.
Competitive Landscape
- Google Assistant
- Amazon Alexa
- Microsoft Cortana
- OpenAI Whisper
- DeepMind Sparrow
Implementation Challenges
- Integration complexity with existing voice platforms
- Ensuring accuracy and reliability of mid-turn interruptions
- User acceptance of AI interruptions during speech
Validation Strategy
- Pilot integration with voice assistant platforms to measure latency and interaction improvements
- User studies in EdTech to evaluate interruption accuracy and learning outcomes
- Partnerships with customer support centers to test real-time issue resolution enhancements
Research Paper Overview
SHANKS: Simultaneous Hearing and Thinking for Spoken Language Models
Summary
SHANKS is an inference framework enabling spoken language models to generate unspoken reasoning while listening to user speech, allowing real-time interaction and interruption during the user's turn. It improves response latency and task completion by reasoning on streaming speech chunks and deciding when to interrupt or call tools before the user finishes speaking.