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Less is More?

Less is More?. Yi Wu Advisor: Alex Rudnicky. People:. There is no data like more data!. Goal: Use less to Perform more. Identifying an informative subset from a large corpus for Acoustic Model (AM) training. Expectation of the Selected Set Good in Performance Fast in Selection.

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Less is More?

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  1. Less is More? Yi Wu Advisor: Alex Rudnicky

  2. People: There is no data like more data!

  3. Goal: Use less to Perform more • Identifying an informative subset from a large corpus for Acoustic Model (AM) training. • Expectation of the Selected Set • Good in Performance • Fast in Selection

  4. Motivation • The improvement of system will become increasingly smaller when we keep adding data. • Training acoustic model is time consuming. • We need some guidance on what is the most needed data.

  5. Approach Overview • Applied to well-transcribed data • Selection based on transcription • Choose subset that have “uniform” distribution on speech unit (word, phoneme, character)

  6. k Gaussian distribution with known priorωi and unknown density function fi(μi ,σi) How to sample data wisely?--A simple example

  7. How to sample wisely?--A simplified example • We are given access to at most N examples. • We have right to choose how much we want from each class. • We train the model use MLE estimator. • When a new sample generated, we use our model to determine its class. Question: How to sample to achieve minimum error?

  8. The optimal Bayes Classifier If we have the exact form of fi(x), above classification is optimal.

  9. To approximate the optimal • We use our MLE • The true error would be bounded by optimal Bayes error plus error bound for our worst estimated

  10. Sample Uniformly • We want to sample each class equally. • The data selected will have good coverage on each class. • This will give robust estimation on each class.

  11. The Real ASR system

  12. Data Selection for ASR System • The prior has been estimated independently by language model. • To make acoustic model accurate, we want to sample the W uniformly. • We can take the unit to be phoneme, character, word. We want their distribution to be uniform.

  13. Entropy: Measure for “uniformness” • Use the entropy of the word (phoneme) as ways of evaluation • Suppose the word (phoneme) has a sample distribution p1, p2…. pn • Choose subset have maximum -p1*log(p1)-p2*log(p2)-... pn *log(pn)) • Entropy actually is the KL distance from uniform distribution

  14. Computational Issue • It is computational intractable to find the transcription set that maximizes the entropy • Forward Greedy Search

  15. Combination • There are multiple entropies we want to maximize. • Combination Method • Weighted Sum • Add sequentially

  16. Experiment Setup • System: Sphinx III • Feature: 39 dimension MFCC • Training Corpus: Chinese BN 97(30hr)+ GaleY1(810hr data) • Test Set: RT04(60 min)

  17. Experiment 1 ( use word distribution) Table 1

  18. More Result

  19. Experiment 2 (add sequentially with phoneme and character 150hr) Table 2

  20. Experiment 1,2

  21. Experiment 3 (with VTLN) Table 3

  22. Summary • Choose data uniformly according to speech unit • Maximize entropy using greedy algorithm • Add data sequentially Future Work • Combine Multiple Sources • Select Un-transcribed Data

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