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AI Research
arXiv CS.AI·Jun 9·5 min read
Training-Free Intelligibility-Guided Observation Addition for Noisy ASR
TL;DR
Automatic speech recognition (ASR) struggles in noisy environments due to background noise and artifacts from speech enhancement (SE). The proposed intelligibility-guided observation addition (OA) method fuses noisy and SE-enhanced speech using intelligibility estimates to determine fusion weights.
✦ Why It Matters
Engineers can implement intelligibility-guided OA to improve ASR performance in noisy environments without retraining models.
Key Takeaways
- Use intelligibility estimates for better speech recognition in noise.
- Combine noisy and enhanced speech without retraining models.
- Explore diverse SE-ASR combinations for optimal performance.
- Adopt training-free methods to simplify implementation.
How It Works
The intelligibility-guided observation addition method calculates fusion weights based on intelligibility estimates from the ASR system. This allows for a more informed combination of noisy and enhanced speech inputs, improving recognition accuracy without the need for retraining the underlying models.