What desired error rate should be set for a speech recognition system with 14% noisy data?
Case context: A team is building a speech-recognition system for a factory. Upon review, they find that 14% of the collected audio clips are so distorted by machinery noise that even human experts cannot transcribe them accurately. The team's current algorithm achieves a 16% error rate.
Question: Based on the provided case context, what should the engineering team set as their desired error rate proxy, and how does this impact their goal setting for the algorithm?
Sample answer: The team should set their desired optimal error rate proxy around 14%, because human experts cannot surpass this threshold due to the inherent noise in the data. Since the current algorithm is at a 16% error rate, the avoidable bias is only about 2%. Therefore, the team should recognize that a near 0% error rate is impossible without fixing the input data, and they should adjust their goals to focus on closing the remaining 2% gap or improving data quality.
Key points:
- The optimal error rate proxy is 14% due to the unintelligible audio.
- The algorithm's avoidable bias compared to human-level performance is roughly 2%.
- A near 0% error rate is an unrealistic goal for this specific dataset.
Rubric: A strong response will identify 14% as the proxy for the optimal error rate, calculate the avoidable bias as 2%, and conclude that pushing for a 0% error rate on this dataset is an unrealistic goal.
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References
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
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Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
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