[src,scripts] Cosmetic,file-mode fixes; fix to nnet1 align.sh introduced in #3383

egs/wsj/s5/steps/nnet/align.sh

@@ -82,8 +82,8 @@ D=$srcdir
 # Create the feature stream,
 feats="ark,s,cs:copy-feats scp:$sdata/JOB/feats.scp ark:- |"
 # apply-cmvn-online (optional),
-[ -n "$online_cmvn_opts" -a ! -f $nndir/global_cmvn_stats.mat ] && echo "$0: Missing $nndir/global_cmvn_stats.mat" && exit 1
-[ -n "$online_cmvn_opts" ] && feats="$feats apply-cmvn-online $online_cmvn_opts --spk2utt=ark:$srcdata/spk2utt $nndir/global_cmvn_stats.mat ark:- ark:- |"
+[ -n "$online_cmvn_opts" -a ! -f $D/global_cmvn_stats.mat ] && echo "$0: Missing $D/global_cmvn_stats.mat" && exit 1
+[ -n "$online_cmvn_opts" ] && feats="$feats apply-cmvn-online $online_cmvn_opts --spk2utt=ark:$srcdata/spk2utt $D/global_cmvn_stats.mat ark:- ark:- |"
 # apply-cmvn (optional),
 [ -n "$cmvn_opts" -a ! -f $sdata/1/cmvn.scp ] && echo "$0: Missing $sdata/1/cmvn.scp" && exit 1
 [ -n "$cmvn_opts" ] && feats="$feats apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp ark:- ark:- |"

src/feat/mel-computations.cc

@@ -262,7 +262,7 @@ void ComputeLifterCoeffs(BaseFloat Q, VectorBase<BaseFloat> *coeffs) {
 // Durbin's recursion - converts autocorrelation coefficients to the LPC
 // pTmp - temporal place [n]
 // pAC - autocorrelation coefficients [n + 1]
-// pLP - linear prediction coefficients [n] (predicted_sn = sum_1^P{a[i] * s[n-i]}})
+// pLP - linear prediction coefficients [n] (predicted_sn = sum_1^P{a[i-1] * s[n-i]}})
 //       F(z) = 1 / (1 - A(z)), 1 is not stored in the demoninator
 BaseFloat Durbin(int n, const BaseFloat *pAC, BaseFloat *pLP, BaseFloat *pTmp) {
   BaseFloat ki;                // reflection coefficient

src/feat/mel-computations.h

@@ -148,7 +148,7 @@ void ComputeLifterCoeffs(BaseFloat Q, VectorBase<BaseFloat> *coeffs);
 // Durbin's recursion - converts autocorrelation coefficients to the LPC
 // pTmp - temporal place [n]
 // pAC - autocorrelation coefficients [n + 1]
-// pLP - linear prediction coefficients [n] (predicted_sn = sum_1^P{a[i] * s[n-i]}})
+// pLP - linear prediction coefficients [n] (predicted_sn = sum_1^P{a[i-1] * s[n-i]}})
 //       F(z) = 1 / (1 - A(z)), 1 is not stored in the denominator
 // Returns log energy of residual (I think)
 BaseFloat Durbin(int n, const BaseFloat *pAC, BaseFloat *pLP, BaseFloat *pTmp);