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Subject : Signal bias removal

Subject : Signal bias removal. Why ? An acoustical mismatch between the training and the testing conditions of hidden Markov model (HMM)-based speech recognition systems . Mismatch 如何造成的呢 ??. S (t). S’(t). H(t). O. *bias 的影嚮  mean shift variance 變大. How ?.

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Subject : Signal bias removal

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  1. Subject : Signal bias removal

  2. Why ? An acoustical mismatch between the training and the testing conditions of hidden Markov model (HMM)-based speech recognition systems . Mismatch如何造成的呢?? S (t) S’(t) H(t) O *bias的影嚮 mean shift variance變大

  3. How ? The bias removal method based on ML 先考慮不知道bias這個參數的likehood P(X|Λ) =Пmax P(x |ג ) t i t i x t X1-u=b1 X2-u=b2 . . b=1/NΣ(xi-u)

  4. 考慮有常數 b y = x + b p(Y|b)=p(Y-b|) p(Y|b,Λ)=Пmaxp(y-b|ג) b=1/NΣ(yi-u) bias X1=y1-b1 Y2=x1=y1-b1 X2=y2-b2 Y3=x2=y2-b2 X3=y3-b3 新feature Iteration 2 新feature Iteration 3 X3 + HMM Compact model

  5. begin sbr_train.c it It > 4 1 0 • Sequential VQ • generate 32 codewords • of MAT-database Write codewords to codebook For all spks • Open files • len, fa ,tab ,bias For all good utterances • read Nframe • read tab • For all frames • Read features • Bias • compute bias for • each utterance • and iteration 20 • times Write out bias end

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