Summary of Digits Micro-model For Accurate and Secure Transactions, by Chirag Chhablani et al.
Digits micro-model for accurate and secure transactions
by Chirag Chhablani, Nikhita Sharma, Jordan Hosier, Vijay K. Gurbani
First submitted to arxiv on: 2 Feb 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 The paper proposes the development of smaller, specialized “micro” Automatic Speech Recognition (ASR) models that can be trained to perform well on specific tasks, such as recognizing multi-digit numbers. These micro-models are designed to require fewer resources and less training data compared to larger general-purpose ASR models like Google STT or OpenAI’s Whisper. The proposed approach uses carefully selected and curated datasets, allowing for high accuracy, agility, and ease of retraining while consuming low compute resources. The micro-model is tested on recognizing digits with an error rate of 1.8%, outperforming the best-of-breed commercial or open-source ASRs, including Whisper. Additionally, the micro-model has a memory footprint of 0.66 GB VRAM, compared to 11 GB VRAM for Whisper. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates smaller, special “micro” Automatic Speech Recognition (ASR) models that can recognize numbers better than big models. These small models are trained on less data and take less time and resources to train. They’re also good at recognizing different speaking styles and can be used in real-world situations. |
