ICASSP 2006 Robustness Techniques Survey

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ICASSP 2006 Robustness Techniques Survey
ShihHsiang 2006
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PARAMETRIC NONLINEAR FEATURE EQUALIZATIONFOR
ROBUST SPEECH RECOGNITIONLuz Garcia, Jose C.
Segura, Javier Ramirez, Angel de la Torre,
Carmen BenitezDpto. Teoria de la Senal,
Telematica y Comunicaciones (TSTC)Universidad
de Granada
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Introduction

• HEQ have been successfully applied to deal with
  the nonlinear effect of the acoustic environment
  in the feature domain
• Normalize the probability distributions of the
  features in such a way that the acoustic
  environment effects are (partially) removed
• HEQ still suffer from several limitations
• Rely on a local estimation of the probability
  distributions of the features based on a reduced
  number of observations belonging to a single
  utterance to be equalized
• Nonlinear transformation is based on mapping the
  global CDF of each feature into a reference one
• The transformations are usually based on a
  component-by-component equalization of the
  feature vector, thus discarding any
  cross-information between features in the
  equalization process

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Introduction (cont.)

• In this paper, a parametric nonlinear
  equalization technique is proposed
• Relies on a two Gaussian model for the
  probability distribution of the features
• And on a simple Gaussian classifier to label the
  input frames as belonging to the speech or
  non-speech classes
• Recognition experiments on the AURORA 4 database
  have been performed and the effectiveness of the
  algorithm is analyzed in comparison with other
  linear and nonlinear feature equalization
  techniques

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Review Histogram Equalization

• For a given random variable y with probability
  density function , a function
  mapping into a reference distribution

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Review Histogram Equalization (cont.)

• The relative content of non-speech frames is a
  cause of variability in the HEQ transformation
• because an estimation of the global probability
  distribution is used, that takes into account
  both speech and non-speech frames

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Review Histogram Equalization (cont.)

• The unwanted variability of the transformation
  induced by the variable proportion of non-speech
  frames of each utterance can be
• Reduced by removing non-speech frames before the
  estimation of the transformation
• Another possibility is to use different
  transformations for speech and non-speech frames
• Instead of using a transformation to map the
  global CDFs of the features, we can build
  separate mappings for speech and non-speech
  frames
• As an alternative, the propose the use of a
  parametric form of the equalization transform
  based on a two Gaussian mixture model
• The first Gaussian is used to represent
  non-speech frames, while the second one
  represents speech frames

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Two-class parametric equalization

• For each class, a parametric linear
  transformation is defined to map the clean and
  noisy representation spaces
• The clean Gaussians for speech and non-speech
  frames can be estimated from the training
  database, while the noisy Gaussians should be
  estimated from the utterance to be equalized

noise
non-speech
clean Gaussian
noisy speech
speech
noisy Gaussian
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Two-class parametric equalization (cont.)

• In order to select whether the current frame y is
  speech or non-speech, a voice activity detector
  could be used
• Implies a hard decision between both linear
  transformations that could create discontinuities
  in the limit of the non-speech/speech decision
• Instead, a soft decision can be used

The posterior probabilities P(ny) and P(sy) are
obtained using a simple two-class Gaussian
classifier on MFCC C0
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Two-class parametric equalization (cont.)

• Training the two-class Gaussian classifier
• Initially, those frames with C0 below the mean
  value are assigned to the non-speech class and
  those with C0 above the mean are assigned to the
  speech class
• The EM algorithm is then iterated until
  convergence (usually, 10 iterations are enough)
  to obtain the final classifier
• This classifier is used to obtain the class
  probabilities P(ny) and P(sy) and also to
  obtain the mean and covariance matricesµn,y?Sn,y?µ
  s,y and Ss,y for the non-speech and speech
  classes for the given noisy input utterance

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Two-class parametric equalization (cont.)
The two Gaussian model for the C0 and C1 cepstral
coefficients (used as reference model) along with
the histograms of the speech and non-speech
frames for a set of clean utterances
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Experimental Results

• The proposed parametric equalization algorithm
  has been tested on the AURORA4 (WSJ0) database
• The recognition system used in all cases is based
  on continuous crossword triphone models with 3
  tied states and a mixture of 6 Gaussians per
  state
• The language model is the standard bigram for the
  WSJ0 task
• A feature vector of 13 cepstral coefficients is
  used as the basic parameterization of the speech
  signal using C0 instead of the logarithmic energy
• The baseline reference system (BASE) uses
  sentence-by-sentence subtraction of the mean
  values of each cepstral coefficient (CMS)
• The parameters of the reference distribution have
  been obtained by averaging over the whole clean
  training set of utterances

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Experimental Results

• First row (BASE) corresponds to the baseline
  system which is based on a simple CMS linear
  normalization technique.
• The second row (HEQ) shows the word error rates
  when using a standard quantile-based
  implementation of HEQ
• relative word error reduction of 17.8
• The performance of HEQ is clearly improved by PEQ
  as shown in the third row, with a relative word
  error reduction of 30.8.
• This result is very close to the one obtained for
  the AFE, which yields a 31.4 reduction of the
  word error rate
• Moreover, PEQ outperforms AFE in half of the
  tests (i.e. 02, 06, 08, 09, 10, 11 and 13).

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Conclusions and Future Work

• The transformation is based on a nonlinear
  interpolation of two independent linear
  transformations
• The linear transformations are obtained using a
  simple Gaussian model for the classes of speech
  and non-speech features
• The technique evaluated on a complex continuous
  speech recognition task showing its competitive
  performance against linear and nonlinear feature
  equalization techniques like CMS and HEQ
• A study of influence of within class
  cross-correlations is currently under development

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MODEL-BASED WIENER FILTER FOR NOISE ROBUST SPEECH
RECOGNITIONTakayuki Arakawa, Masanori Tsujikawa
and Ryosuke IsotaniMedia and Information
Research Laboratories, NEC Corporation,
Japant-arakawa_at_cp.jp.nec.com, tujikawa_at_cb.jp.nec.
com, r-isotani_at_bp.jp.nec.com
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Introduction

• Various kinds of background noise exist in the
  real world
• Therefore robustness against various kinds of
  noise is quite important.
• Several approaches have been proposed to deal
  with this issue
• Signal-processing-based spectral enhancement
• Spectral Subtraction (SS), Wiener Filter (WF)
• Less computational costs, but needs many tuning
  costs depending on the kind of noise and
  signal-to-noise ratio (SNR)
• Statistical-model-based noise adaptation
• The acoustic model i.e., a hidden Markov model
  (HMM), is adapted to the noisy environment
• It needs huge computational costs to adapt the
  distributions to a noisy environment

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Introduction (cont.)

• Statistical-model-based compensation
• Using Gaussian mixture model (GMM)
• The computational cost is still much more than
  that of the signal-processing-based spectral
  enhancement.
• In this paper, they proposed Model-Based Wiener
  filter (MBW)

Concept
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Proposed Method (Cont.)

• A GMM with K Gaussian distributions is used as
  knowledge of clean speech in the cepstrum domain

MBW algorithm
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Proposed Method (Cont.)

• The noisy speech signal X(t) is modeled as
• Step 1 Perform Spectral Subtraction (SS)
• Step 2 Derive the expected value of the clean
  speech

noisy speech
clean speech
noise
Spectrum Domain
temporary clean speech
estimated noise
flooring parameter
Cepstrum Domain
MMSE Estimation
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Proposed Method (Cont.)

• Step 3 Calculate Wiener Gain
• Step 4 Get the final estimated clean speech

smoothing parameter
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Experiments and Results

• Experiment Condition
• The Mel-frequency cepstral coefficients (MFCC)
  and their 1st and 2nd derivatives are used as
  feature value of speech (include C0)
• The feature value for GMM is composed of a
  13-dimensional MFCC only
• The flooring parameter a is set at 0.1, and the
  smoothing parameter ß is set at 0.98
• The MBW method was tested on the Aurora2-J task
• contains utterances (in Japanese) of consecutive
  digit string recorded in clean environments
• The other conditions are the same as Aurora2

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Experiments and Results (cont.)

• The performance of different mixture number of
  the GMM

At the point of 128 or 256, it becomes saturated
5dB restaurant noise
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Experiments and Results (cont.)

• The word accuracy for each SNR

almost equivalent to that of the AFE.
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Experiments and Results (cont.)

• The Word Accuracy over the SNR for each kind of
  noise.

These results show that the proposed method is
much more robust than the AFE against various
kinds of noise.
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Conclusions

• Review MBW algorithm
• Roughly estimates clean speech signals using SS
• Compensates them using a GMM to improve
  robustness against non-stationary noise
• The compensated speech signal is used to
  calculate the Wiener gain
• Performing Wiener filtering
• The results show that the proposed method
  performs as well as the ETSI AFE
• These results demonstrate that the proposed
  method is robust against various kinds of noise