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Signal Bias Removal in HMM-based Speech Recognition Systems

Learn how acoustical mismatches in training and testing can affect speech recognition biases and how bias removal methods based on ML can enhance system accuracy. Dive into the iterative process of bias computation and removal.

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Signal Bias Removal in HMM-based Speech Recognition Systems

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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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