Efficient Punctuation Restoration via Weighted Lookahead Scoring Method for Streaming ASR Systems
arXiv:2606.05179v1 Announce Type: new Abstract: Punctuation restoration improves ASR (Automatic Speech Recognition) readability. However streaming ASR requires online decisions with limited future context. In streaming ASR, the system predicts punctuation incrementally, which makes generation-based approaches prone to latency and alignment failures under boundary-wise evaluation. This paper proposes a non-autoregressive scoring method (no free-form generation) that preserves the input transcript and makes a decision at each word boundary. Our method compares punctuation insertion hypotheses ag
The continuous improvement in ASR systems and their increasing deployment in real-world, streaming applications necessitates efficient solutions for enhancing readability and usability.
Improved punctuation in streaming ASR makes voice interfaces more natural and functional, critical for widespread adoption in various industries.
This advancement enables more accurate and less latent real-time transcription, directly improving user experience and system reliability in voice-controlled environments.
- · AI voice assistant providers
- · Customer service platforms
- · Speech-to-text service companies
- · Disabled user accessibility platforms
- · Competitors with less efficient ASR punctuation methods
- · Transcription services relying on manual correction of ASR output
Real-time transcription becomes more reliable and easier to read.
Increased adoption of voice interfaces and AI assistants across more complex tasks due to improved conversational flow.
Further blurring of the line between human and AI communication, enhancing multimodal interaction paradigms.
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Read at arXiv cs.CL