Summary of Personalized Speech Recognition For Children with Test-time Adaptation, by Zhonghao Shi et al.
Personalized Speech Recognition for Children with Test-Time Adaptation
by Zhonghao Shi, Harshvardhan Srivastava, Xuan Shi, Shrikanth Narayanan, Maja J. Matarić
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to automatic speech recognition (ASR) for children is proposed, addressing the issue of off-the-shelf ASR models not generalizing well to children’s speech. The method involves unsupervised test-time adaptation (TTA) to adapt pre-trained adult-speech models to each child speaker without requiring additional human annotations. Experimental results show significant improvements over unadapted baselines for both average and individual child speakers, highlighting the importance of TTA in real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is being developed to help computers understand children’s voices correctly. Right now, computer systems trained on adult voices struggle to recognize kids’ speech. To fix this, a special technique called test-time adaptation (TTA) will be used. This method allows pre-trained models to adjust and improve when they encounter a new child’s voice without needing more human help. The results show that using TTA makes the computer systems much better at understanding kids’ voices. |
Keywords
» Artificial intelligence » Unsupervised
