speech enhancement for asr by hans hwang 8/23/2000 reference 1. alan v. oppenheim,etc., ”...
TRANSCRIPT
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 an
d Recognition”,IEEE Trans. On SAP,138-151,1999 3.Ing Yang Soon,etc.,”Noisy Speech Enhancement Using Discret
e Cosine Transform”,Speech communication,249-257,1998
Outline Signal Separation by S-ADF/LMS Speech Enhancement by DCT Residual Signal Reduction Experimental Results
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
SSS cont’d 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
1
1
B
AH
SSS (cont’d) Source separation system (separate source signals out from acquired signals)
and called decoupling filters and modeled as FIR filter
1
1
1
11
B
A
ABH
^
A
^^
1 BAC
^
B
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
SSS by ADF (cont’d) The closed form of decoupling filters
where
SSS by ADF (cont’d) 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. -
trt )(
SSS by ADF (cont’d) The experiment of : )(t
tt /5)( tttt /)(&/2.0)(
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.
SSD (cont’d)
SSD (cont’d) SSD by coherence function
If then If then
EE KK ,2,1 0)( k
EEEE KKKKor
,2,1,2,1 1)( k
SSD (cont’d) - decision variable
-Decision Rule:
SSD (cont’d)-Implementation using DFT and Result
SSD (cont’d)
Improved Filter Estimation Widrow’s LMS algorithm proposed in 1975 -If we don’t 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.
Improved Filter Estimation LMS algorithm and result
Experimental Results-Evaluate in terms of WRA and SIR
Experimental Result (cont’d) *Use 717 TIMIT
sentences to train 62 phone units.
Front-end feature is PLP and its dynamic. Grammar perplexity is
105.
After acoustic normalization
Speech Enhancement usingDiscrete Cosine Transform
Motivation -DCT provides significantly higher compaction as
compared to the DFT
SE Using DCT (cont’d) -DCT provides higher spectral resolution than DFT -DCT is real transform so it has only binary phases. Its phase won’t be changed unless added noise is strong.
1
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1
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)2
)12(cos()()()(
)2
)12(cos()()()(
N
k
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knkXknx
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knnxkkX
Nk
N
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2)(&
1)0(
1,0
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. -
,
)](/)([)(^
KYkXEkX
by Bayes’ ruleand signal model 1)(
)()(
^
k
kkX
])([
])([
)(
)()( 2
2
kNE
kXE
k
kk
n
x
MMSE (cont’d) Estimating signal source by Decision Directed Estimation(DDE) (proposed by Ephraim & Malah in ‘8
4)
= 0.98 in computer simulation
}0),()(max{)1()()( 2^^
kkYkk npxx
Reduction of Residual Signal
Introduction -If the source signal more likely exists then the
estimated is more reliable. -two states of inputs H0:speech absent
H1:speech present
: modified filter output
)())(/()(^
1 kXkYHPkA
Reduction of Residual Signal
- where
)/)(()()/)(()(
)/)(()())(/(
2211
111 HkYpHpHkYpHp
HkYpHpkYHp
))()(,0);(()/)((
))(,0);(()/)((
1
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kknYNHkYp
kkYNHkYp
xn
n
)(1)))()((2)()(
exp(1
1))(/( 21
kkknkYk
kYHp
x
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
n
x x f
x f
n2
) (
2
) (
^
log 101
Experimental Results