speech enhancement for asr
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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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
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OutlineSignal Separation by S-ADF/LMSSpeech Enhancement by DCTResidual Signal ReductionExperimental Results
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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
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SSS contdTwo-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
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SSS (contd)Source separation system (separate source signals out from acquired signals)
and called decoupling filters and modeled as FIR filter
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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
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SSS by ADF (contd)The closed form of decoupling filters
where
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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. -
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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.
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SSD (contd)
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SSD (contd)SSD by coherence function
If then If then
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SSD (contd) - decision variable
-Decision Rule:
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SSD (contd)-Implementation using DFT and Result
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SSD (contd)
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Improved Filter EstimationWidrows 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.
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Improved Filter EstimationLMS algorithm and result
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Experimental Results-Evaluate in terms of WRA and SIR
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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
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Speech Enhancement usingDiscrete Cosine TransformMotivation -DCT provides significantly higher compaction as compared to the DFT
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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.
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Estimating signal by MMSEIntorduction -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 ruleand signal model
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MMSE (contd)Estimating signal source by Decision Directed Estimation(DDE) (proposed by Ephraim & Malah in 84)
= 0.98 in computer simulation
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Reduction of Residual SignalIntroduction -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
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Reduction of Residual Signal - where
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Experimental Results Measure in Segmental SNRWhite noise addedFan noise added
*EMFDETFDETF26.2711.9311.8211.27-10.17-0.071.932.09-1.0511.3413.6913.32-21.99-6.99-0.040.95
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Experimental Results