ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition
Learn how ASKD-Whisper achieves efficient and low-latency Automatic Speech Recognition through adaptive self-knowledge distillation, improving upon traditional knowledge distillation methods
- Implement knowledge distillation (KD) in an ASR system to compress large-scale foundation models
- Apply adaptive self-knowledge distillation to dynamically adjust the distillation process
- Evaluate the performance of the ASKD-Whisper approach using metrics such as word error rate (WER) and latency
- Compare the results with traditional KD methods to assess the improvements
- Fine-tune the ASKD-Whisper model for specific ASR tasks or datasets
Researchers and engineers working on Automatic Speech Recognition (ASR) systems can benefit from this approach to improve model efficiency and latency, while also being relevant to AI engineers and ML researchers interested in knowledge distillation techniques
💡 Adaptive self-knowledge distillation can effectively balance the trade-off between model size and performance in ASR systems
🗣️ Improve ASR efficiency and latency with ASKD-Whisper, an adaptive self-knowledge distillation approach! 🚀
Key Takeaways
Learn how ASKD-Whisper achieves efficient and low-latency Automatic Speech Recognition through adaptive self-knowledge distillation, improving upon traditional knowledge distillation methods
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Abstract:
arXiv:2601.19919v2 Announce Type: replace-cross Abstract: Knowledge distillation (KD) is one of the most effective paradigms for compressing large-scale foundation models into deployable architectures. In the context of Automatic Speech Recognition (ASR), previous studies have predominantly focused on forcing the student model to strictly mimic the predictive distribution of a massive teacher model. However, this static dependency often presents an inherent trade-off: while the student rapidly a
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