A Survey of Boosting HMM Acoustic Model Training

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A Survey of Boosting HMM Acoustic Model Training
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Introduction

• The No Free Lunch Theorem states that
• There is no single learning algorithm that in any
  domain always induces the most accurate learner
• Learning is an ill-posed problem and with finite
  data, each algorithm converges to a different
  solution and fails under different circumstances
• Though the performance of a learner may be
  fine-tuned, but still there are instances on
  which even the best learner is not accurate
  enough
• The idea is..
• There may be another learner that is accurate on
  these instances
• By suitably combining multiple learners then,
  accuracy can be improved

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Introduction

• Since there is no point in combining learners
  that always make similar decisions
• The aim is to be able to find a set of
  base-learners who differ in their decisions so
  that they will complement each other
• There are different ways the multiple
  base-learners are combined to generate the final
  outputs
• Multiexpert combination methods
• Voting and its variants
• Mixture of experts
• Stacked generalization
• Multistage combination methods
• Cascading

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Voting

• The simplest way to combine multiple classifiers
• which corresponds to taking a linear combination
  of the learners
• this is also known as ensembles and linear
  opinion pools
• The name voting comes from its use in
  classification
• if , called
  plurality voting
• if , called
  majority voting

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Bagging

• Bagging is a voting method whereby base-learners
  are made different by training them over slightly
  different training sets
• is done by bootstrap
• where given a training set X of size N, we draw N
  instances randomly from X with replacement
• In bagging, generating complementary
  base-learners is left to chance and to the
  instability of the learning method
• A learning algorithm is an unstable algorithm if
  small changes in the training set causes a large
  difference in the generated learner
• decision trees, multilayer perceptrons, condensed
  nearest neighbor
• Bagging is short for Bootstrap aggregating

Breiman, L. 1996. Bagging Predictors. Machine
Learning 26, 123-140
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Boosting

• In boosting, we actively try to generate
  complementary base-learners by training the next
  learner on the mistakes of the previous learners
• The original boosting algorithms (Schapire 1990)
  combines three weak learners to generate a strong
  learner
• In the sense of the probably approximately
  correct (PAC) learning model
• Disadvantage
• It requires a very large training sample

Schapire, R.E. 1990. The Strength of Weak
Learnability. Machine Learning 5, 197-227
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AdaBoost

• AdaBoost, short for adaptive boosting, uses the
  same training set over and over and thus need not
  be large and it can also combine an arbitrary
  number of base-learners, not three
• The idea is to modify the probabilities of
  drawing the instances as a function of the error
• The probability of a correctly classified
  instance is decreased, then a new sample set is
  drawn from the original sample according to these
  modified probabilities
• That focuses more on instances misclassified by
  previous learner
• Schapire et al. explain that the success of
  AdaBoost is due to its property of increasing the
  margin
• Schapire. et al. 1998. Boosting the Margin A
  New Explanation for Effectiveness of Voting
  Methods Annals of Statistics 26, 1651-1686

Freund and Schapire. 1996. Experiments with a
New Boosting Algorithm In ICML 13, 148-156
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AdaBoost.M2 (Freund and
Schapire, 1997)
Freund and Schapire. 1997. A decision-theoretic
generalization of on-line learning and an
application to boosting Journal of Computer and
System Sciences 55, 119-139
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Evolution of Boosting Algo.
2
4
ICSLP 04R. Zhang A. RudnickyA Frame Level
Boosting Training Scheme for Acoustic Modeling
ICASSP 04C. Dimitrakakis S. BengioBoosting
HMMs with An Application to Speech Recognition
ICSLP 04R. Zhang A. RudnickyApply N-Best
List Re-Ranking to Acoustic Model Combinations of
Boosting Training
E
ICASSP 00G. Zweig M. PadmanabhanBoosting
Gaussian Mixtures in An LVCSR System
3
ICSLP 04R. Zhang A. RudnickyOptimizing
Boosting with Discriminative Criteria
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EuroSpeech 05R. Zhang et al.Investigations on
Ensemble Based Semi-Supervised Acoustic Model
Training
ICASSP 99H. SchwenkUsing Boosting to Improve a
Hybrid HMM/Neural Network Speech Recognizer
1999
1996
2003
2002
1997
2000
2004
2005
2006
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ICSLP 06R. Zhang A. Rudnicky Investigations
of Issues for Using Multiple Acoustic Models to
Improve CSR
B
ICSLP 96G. Cook T. RobinsonBoosting the
Performance of Connectionist LVSR
Neural Network
SpeechCom 06 C. Meyer H. Schramm Boosting HMM
Acoustic Models in LVCSR
0
ICASSP 03R. Zhang A. RudnickyImproving the
Performance of An LVCSR System Through Ensembles
of Acoustic Models
GMM
EuroSpeech 97G. Cook et al.Ensemble Methods
for Connectionist Acoustic Modeling
HMM
1
EuroSpeech 03 R. Zhang A. Rudnicky Comparative
Study of Boosting and Non-Boosting Training for
Constructing Ensembles of Acoustic Models
D
ICASSP 02C. MeyerUtterance-Level Boosting of
HMM Speech Recognition
A
ICASSP 02 I. Zitouni et al. Combination of
Boosting and Discriminative Training for Natural
Language Call Steering Systems
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Improving The Performance of An LVCSR System
Through Ensembles of Acoustic Models

• ICASSP 2003
• Rong Zhang and Alexander I. Rudnicky
• Language Technologies Institute,
• School of Computer Science
• Carnegie Mellon University

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Bagging vs. Boosting

• Bagging
• In each round, bagging randomly selects a number
  of examples from the original training set, and
  produces a new single classifier based on the
  selected subset
• The final classifier is built by choosing the
  hypothesis best agreed on by single classifiers
• Boosting
• In boosting, the single classifiers are
  iteratively trained in a fashion such that
  hard-to-classify examples are given increasing
  emphasis
• A parameter that measures the classifiers
  importance is determined in respect of its
  classification accuracy
• The final hypothesis is the weighted majority
  vote from the single classifiers

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Algorithms

• The first algorithm is based on the intuition
  that an incorrectly recognized utterance should
  receive more attention in training
• If the weight of an utterance is 2.6, we first
  add two copies of the utterance to the new
  training set, and then add its third copy with
  probability 0.6

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Algorithms

• The exponential increase in the size of training
  set is a severe problem for algorithm 1
• Algorithm 2 is proposed to address this problem

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Algorithms

• In algorithm 1 and 2, there is no concern to
  measure how important a model is relative to
  others
• Good model should play more important role than
  bad one

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Experiments

• Corpus CMU Communicator system
• Experimental results

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Comparative Study of Boosting and Non-Boosting
Training for Constructing Ensembles of Acoustic
Models

• Rong Zhang and Alexander I. Rudnicky
• Language Technologies Institute, CMU
• EuroSpeech 2003

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Non-Boosting method

• Bagging
• is a commonly used method in machine learning
  field
• randomly selects a number of examples from the
  original training set and produces a new single
  classifier
• in this paper, we call it a non-Boosting method
• Based on the intuition
• The misrecognized utterance should receive more
  attention in the successive training

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Algorithms
? is a parameter that prevents the size of the
training set from being too large.
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Experiments

• The corpus
• Training set 31248 utterances Test set 1689
  utterances

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A Frame Level Boosting Training Scheme for
Acoustic Modeling

• ICSLP 2004
• Rong Zhang and Alexander I. Rudnicky
• Language Technologies Institute,
• School of Computer Science
• Carnegie Mellon University

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Introduction

• In the current Boosting algorithm, utterance is
  the basic unit used for acoustic model training
• Our analysis shows that there are two notable
  weaknesses in this setting..
• First, the objective function of current Boosting
  algorithm is designed to minimize utterance error
  instead of word error
• Second, in the current algorithm, an utterance is
  treated as a unity for resample
• This paper proposes a frame level Boosting
  training scheme for acoustic modeling to address
  these two problems

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Frame Level Boosting Training Scheme

• The metrics that we will use in Boosting training
  is the frame level conditional probability
  -----(word level)
• Objective function

is the pseudo
loss for frame t, which describes the degree of
confusion of this frame for recognition
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Frame Level Boosting Training Scheme

• Training Scheme
• How to resample the frame level training data?
• to duplicate for times and creates
  a new utterance for acoustic model training

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Experiments

• Corpus CMU Communicator system
• Experimental results

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Boosting HMM acoustic models in large vocabulary
speech recognition

• Carsten Meyer, Hauke Schramm
• Philips Research Laboratories, Germany
• SPEECH COMMUNICATION 2006

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Utterance approach for boosting in ASR

• An intuitive way of applying boosting to HMM
  speech recognition is at the utterance level
• Thus, boosting is used to improve upon an initial
  ranking of candidate word sequences
• The utterance approach has two advantages
• First, it is directly related to the sentence
  error rate
• Second, it is computationally much less expensive
  than boosting applied at the level of feature
  vectors

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Utterance approach for boosting in ASR

• In utterance approach, we define the input
  patterns to be the sequence of feature
  vectors corresponding to the entire utterance
• denotes one possible candidate word sequence
  of the speech recognizer, being the correct
  word sequence for utterance
• The a posteriori confidence measure is calculated
  on basis of the N-best list for utterance

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Utterance approach for boosting in ASR

• Based on the confidence values and AdaBoost.M2
  algorithm, we calculate an utterance weight
  for each training utterance
• Subsequently, the weight are used in maximum
  likelihood and discriminative training of
  Gaussian mixture model

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Utterance approach for boosting in ASR

• Some problem encountered when apply it to
  large-scale continuous speech application
• The N-best lists of reasonable length (e.g.
  N100) generally contain only a tiny fraction of
  the possible classification results
• This has two consequences
• In training, it may lead to sub-optimal utterance
  weights
• In recognition, Eq. (1) cannot be applied
  appropriately

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Utterance approach for CSR--Training

• Training
• A convenient strategy to reduce the complexity of
  the classification task and to provide more
  meaningful N-best lists consists in chopping of
  the training data
• For long sentences, it simply means to insert
  additional sentence break symbols at silence
  intervals with a given minimum length
• This reduces the number of possible
  classifications of each sentence fragment, so
  that the resulting N-best lists should cover a
  sufficiently large fraction of hypotheses

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Utterance approach for CSR--Decoding

• Decoding lexical approach for model combination
• A single pass decoding setup, where the
  combination of the boosted acoustic models is
  realized at a lexical level
• The basic idea is to add a new pronunciation
  model by replicating the set of phoneme symbols
  in each boosting iteration (e.g. by appending
  the suffix _t to the phoneme symbol)
• The new phoneme symbols, represent the underlying
  acoustic model of boosting iteration

au, au_1 ,au_2,
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Utterance approach for CSR--Decoding

• Decoding lexical approach for model combination
  (cont.)
• Add to each phonetic transcription in the
  decoding lexicon a new transcription using the
  corresponding phoneme set
• Use the reweighted training data to train the
  boosted classifier
• Decoding is then performed using the extended
  lexicon and the set of acoustic models weighted
  by their unigram prior probabilities which are
  estimated on the training data

sic_a, sic_1 a_1 ,
weighted summation
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In more detail
Training
Training corpus
_t
Boosting Iteration t
Mt
phonetically transcribed
training corpus(Mt)
ML/MMI training
pronunciation variant
sic_a, sic_1 a_1 ,
Decoding
Lexicon
M1,M2,,Mt
unweighted model combination weighted model
combination
extend
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In more detail
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Weighted model combination

• Word level model combination

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Experiments

• Isolated word recognition
• Telephone-bandwidth large vocabulary isolated
  word recognition
• SpeechDat(II) German meterial
• Continuous speech recognition
• Professional dictation and Switchboard

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Isolated word recognition

• Database
• Training corpus consists of 18k utterances
  (4.3h) of city, company, first and family names
• Evaluations
• LILI test corpus 10k single word utterances
  (3.5h) 10k words lexicon (matched conditions)
• Names corpus an inhouse collection of 676
  utterances (0.5h) two different decoding lexica
  10k lex, 190k lex (acoustic conditions are
  matched, whereas there is a lexical mismatch)
• Office corpus 3.2k utterances (1.5h), recorded
  over microphone in clean conditions 20k lexicon
  (an acoustic mismatch to the training conditions)

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Isolated word recognition

• Boosting ML models

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Isolated word recognition

• Combining boosting and discriminative training
• The experiments in isolated word recognition
  showed that boosting may improve the best test
  error rates

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Continuous speech recognition

• Database
• Professional dictation
• An inhouse data collection of real-life
  recordings of medical reports
• The acoustic training corpus consists of about
  58h of data
• Evaluations were carried out on two test corpora
• Development corpus consists of 5.0h of speech
• Evaluation corpus consists of 3.3h of speech
• Switchboard
• Consisting of spontaneous conversations recorded
  over telephone line 57h(73h) of male(female)
• Evaluations corpus
• Containing about 1h(0.5h) of male(female)

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Continuous speech recognition

• Professional dictation

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• Switchboard

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Conclusions

• In this paper, a boosting approach which can be
  applied to any HMM based speech recognizer was be
  presented and evaluated
• The increased recognizer complexity and thus
  decoding effort of the boosted systems is a major
  drawback compared to other training techniques
  like discriminative training

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Probably Approximately Correct Learning

• We would like our hypothesis to be approximately
  correct, namely, that the error probability be
  bounded by some value
• We also would like to be confident in our
  hypothesis in that we want to know that our
  hypothesis will be correct most of the time, so
  we want to be probably correct as well
• Given a class, , and examples drawn from
  some unknown but fixed probability distribution,
  such that with probability at least ,
  the hypothesis has error at most , for
  arbitrary and

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Probably Approximately Correct Learning

• How many training examples N should we have, such
  that with probability at least 1 ? d, h has error
  at most e ?

most specific hypothesis, S
most general hypothesis, G

• Each strip is at most e/4
• Pr that we miss a strip 1? e/4
• Pr that N instances miss a strip (1 ? e/4)N
• Pr that N instances miss 4 strips 4(1 ? e/4)N
• 4(1 ? e/4)N d and (1 ? x)exp( ? x)
• 4exp(? eN/4) d and N (4/e)log(4/d)

h Î H, between S and G is consistent and make
up the version space