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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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**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**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.**Approach Overview**• Applied to well-transcribed data • Selection based on transcription • Choose subset that have “uniform” distribution on speech unit (word, phoneme, character)**k Gaussian distribution with known priorωi and unknown**density function fi(μi ,σi) How to sample data wisely?--A simple example**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?**The optimal Bayes Classifier**If we have the exact form of fi(x), above classification is optimal.**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**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.**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.**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**Computational Issue**• It is computational intractable to find the transcription set that maximizes the entropy • Forward Greedy Search**Combination**• There are multiple entropies we want to maximize. • Combination Method • Weighted Sum • Add sequentially**Experiment Setup**• System: Sphinx III • Feature: 39 dimension MFCC • Training Corpus: Chinese BN 97(30hr)+ GaleY1(810hr data) • Test Set: RT04(60 min)**Experiment 2 (add sequentially with phoneme and character**150hr) Table 2**Experiment 3 (with VTLN)**Table 3**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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