Idea
A streaming sequence-to-sequence model enabling real-time speech recognition and synthesis for developers and AI applications.
Research Paper
Core Innovation
This paper presents Delayed Streams Modeling, which shifts alignment to a preprocessing step and models time-aligned streams with a decoder-only language model. Unlike prior streaming methods that learn policies to decide when to read or write, DSM uses fixed delays to enable streaming inference for arbitrary output sequences from any input combination. This approach simplifies streaming sequence-to-sequence learning and achieves competitive performance with offline baselines.
Market Size (TAM)
$10–20B TAM, $2–5B SAM; assumption: large global market for speech recognition, synthesis, and real-time AI interfaces.
Potential Customers & Pain Points
- Speech technology companies needing low-latency ASR and TTS
- AI developers requiring flexible streaming sequence models
- Enterprises deploying real-time voice interfaces
- Multimedia platforms integrating multimodal streaming
- Researchers seeking scalable sequence-to-sequence solutions
Business Model
Offer DSM as an API and SDK for speech and multimodal streaming applications with tiered pricing based on usage and enterprise features.
Competitive Landscape
- Google Speech-to-Text
- OpenAI Whisper
- Microsoft Azure Speech Services
Implementation Challenges
- Integration complexity with existing pipelines
- Latency optimization for diverse hardware
- Competition from established speech AI providers
Validation Strategy
- Develop prototype API for ASR and TTS streaming
- Conduct benchmarks against leading speech models
- Pilot deployments with select enterprise partners
Research Paper Overview
Streaming Sequence-to-Sequence Learning with Delayed Streams Modeling
Summary
This paper introduces Delayed Streams Modeling (DSM), a method for streaming multimodal sequence-to-sequence learning that uses pre-aligned input and output streams with delays to enable flexible, low-latency inference. DSM supports arbitrary output sequences from any input combination and is demonstrated on automatic speech recognition and text-to-speech tasks, achieving state-of-the-art performance and latency comparable to offline models.