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X2Streaming-ASR introduces a method that waits when uncertain and emits partial transcripts only when ready, optimizing context use for streaming automatic speech recognition. This approach addresses limitations of fixed chunk sizes and delays in existing real-time ASR systems.
X2Streaming-ASR introduces a method that waits when uncertain and emits partial transcripts only when ready, optimizing context use for streaming automatic speech recognition. This approach addresses limitations of fixed chunk sizes and delays in existing real-time ASR systems.
AI-assisted summary based on the listed source.
Streaming automatic speech recognition (ASR) for real-time voice agents and full-duplex dialogue must provide accurate partial transcripts with low commit latency. Existing systems commonly use a fixed chunk size, look-ahead, or target delay, or encourage emissions near estimated acoustic boundaries. These...
Accurate and low-latency partial transcripts are crucial for real-time voice agents and full-duplex dialogue systems. By optimizing emission timing, X2Streaming-ASR can enhance the responsiveness and accuracy of voice-driven applications.
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