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.

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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.


The Real ASR system


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 1 ( use word distribution)

Table 1


More Result


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

Table 2


Experiment 1,2


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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