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About this paper
Shaan Bijwadia, Shuo-yiin Chang, Bo Li, Tara Sainath, Chao Zhang, Yanzhang He
Google researchers developed a unified end-to-end model that integrates Automatic Speech Recognition (ASR) and Endpointing (EP) into a single architecture. This system reduces median endpoint latency by 120ms (30.8%) while maintaining ASR accuracy, addressing computational inefficiency and improving responsiveness in real-time speech applications.
Key insights
- The 'switch connection' mechanism allows the endpointing model to dynamically switch between processing raw audio and leveraging richer latent representations from the ASR encoder, enabling computational flexibility.
- Joint training of ASR and endpointing tasks within a single modified RNN-Transducer architecture fosters shared representations that benefit both functions.
- By training the endpointing model with both raw audio and encoder outputs, the system achieves robustness and can transition seamlessly between resource-efficient EP-only mode and performance-optimized ASR+EP mode during inference.
Summary by alphaXiv.
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