Speech Enhancement for ASR

1
Speech Enhancement for ASR

• by Hans
  Hwang 8/23/2000
• Reference
• 1. Alan V. Oppenheim ,etc.,Multi-Channel
  Signal Separation by
• Decorrelation,IEEE Trans. on
  ASSP,405-413,1993
• 2.Yunxin Zhao,etc.,Adaptive Co-channel Speech
  Separation and
• Recognition,IEEE Trans. On SAP,138-151,1999
• 3.Ing Yang Soon,etc.,Noisy Speech Enhancement
  Using Discrete
• Cosine Transform,Speech communication,249-257
  ,1998

2
Outline

• Signal Separation by S-ADF/LMS
• Speech Enhancement by DCT
• Residual Signal Reduction
• Experimental Results

3
Speech Signal Separation

• Introduction
• -To Recover the desired signal and identify
  the unknown
• system from the observation signal
• -Speech signal recovered from SSS will
  increase SNR and
• improve the speech recognition accuracy
• -Specifically consider the two-channel case

4
SSS contd

• Two-channel model description
• 
  
• A and B are cross-coupling effect between
  channels and
• we ignore the transfer function of each channel.
• xi(t) is source signal and yi(t) is acquired
  signal

5
SSS (contd)

• Source separation system
• (separate source signals out from acquired
  signals)
• and called decoupling filters and modeled as
  FIR filter

6
SSS by ADF

• Calculate the FIR coeff. by adaptive decorre-
• lation filter(ADF) proposed by A. V. Oppenheim
• in 1993
• -The objective is to design decoupling filter
  s.t.,
• the estimated signals are uncorrelated.
• -The decoupling filtering coeff.s are
  estimated
• iteratively based on the previous estimated
• filter coeff.s and current observations

7
SSS by ADF (contd)

• The closed form of decoupling filters
• where

8
SSS by ADF (contd)

• Choice of adaptation gain
• -As time goes to infinite the adaptation gain
• goes to zero for the system stable
  consideration.
• -Optimal choice adaptation gain for the system
• stability and convergence.
• -

9
SSS by ADF (contd)

• The experiment of

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Source Signal Detection(SSD)

• Introduction
• -If one of the two is inactive then the
  estimated
• signals will be poor by ADF and cause the
  recog-
• nition errors.
• -So the ASR and ADF are performed within active
  
• region of each target signal.

11
SSD (contd)
12
SSD (contd)

• SSD by coherence function
• If then
• If then

13
SSD (contd)

• - decision variable
• -Decision Rule

14
SSD (contd)

• -Implementation using DFT and Result

15
SSD (contd)
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Improved Filter Estimation

• Widrows LMS algorithm proposed in 1975
• -If we dont know A or B in observation(i.e.,
  one of
• the source signals is inactive) then the
  estimation
• of filters will cause much errors compared to
  the
• actual filters.
• -If we know source signal 2 is inactive(using
  SSD)
• then we only estimate filter B and remain
  filter A
• unchanged.

17
Improved Filter Estimation

• LMS algorithm and result

18
Experimental Results

• -Evaluate in terms of WRA and SIR

19
Experimental Result (contd)

• Use 717 TIMIT
• sentences to
  train
• 62 phone units.
• Front-end
  feature is
• PLP and its
  dynamic.
• Grammar
  perplexity is
• 105.
• After acoustic
  normalization

20
Speech Enhancement usingDiscrete Cosine Transform

• Motivation
• -DCT provides significantly higher compaction
  as
• compared to the DFT

21
SE Using DCT (contd)

• -DCT provides higher spectral resolution than
  DFT
• -DCT is real transform so it has only binary
  phases.
• Its phase wont be changed unless added noise
  is
• strong.

22
Estimating signal by MMSE

• Intorduction
• -y(t)x(t)n(t) and Y(k)X(k)N(k)
• Assume DCT coeff.s are statistically
  independent
• and estimated signal is less diffenent from the
  
• original signal.
• -
• ,

by Bayes rule and signal model
23
MMSE (contd)

• Estimating signal source by Decision Directed
• Estimation(DDE) (proposed by Ephraim Malah
  in 84)
• 0.98 in computer simulation

24
Reduction of Residual Signal

• Introduction
• -If the source signal more likely exists then
  the
• estimated is more reliable.
• -two states of inputs
• H0speech absent
• H1speech present
• modified filter
  output

25
Reduction of Residual Signal

• -
• where

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Experimental Results

• Measure in Segmental SNR

EMF DETF DETF2
6.27 11.93 11.82 11.27
-10.17 -0.07 1.93 2.09
-1.05 11.34 13.69 13.32
-21.99 -6.99 -0.04 0.95
White noise added
Fan noise added
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Experimental Results