// transform/cmvn.h // Copyright 2009-2013 Microsoft Corporation // Johns Hopkins University (author: Daniel Povey) // See ../../COPYING for clarification regarding multiple authors // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY // KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED // WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE, // MERCHANTABLITY OR NON-INFRINGEMENT. // See the Apache 2 License for the specific language governing permissions and // limitations under the License. #ifndef KALDI_TRANSFORM_CMVN_H_ #define KALDI_TRANSFORM_CMVN_H_ #include "base/kaldi-common.h" #include "matrix/matrix-lib.h" namespace kaldi { /// This function initializes the matrix to dimension 2 by (dim+1); /// 1st "dim" elements of 1st row are mean stats, 1st "dim" elements /// of 2nd row are var stats, last element of 1st row is count, /// last element of 2nd row is zero. void InitCmvnStats(int32 dim, Matrix *stats); /// Accumulation from a single frame (weighted). void AccCmvnStats(const VectorBase &feat, BaseFloat weight, MatrixBase *stats); /// Accumulation from a feature file (possibly weighted-- useful in excluding silence). void AccCmvnStats(const MatrixBase &feats, const VectorBase *weights, // or NULL MatrixBase *stats); /// Apply cepstral mean and variance normalization to a matrix of features. /// If norm_vars == true, expects stats to be of dimension 2 by (dim+1), but /// if norm_vars == false, will accept stats of dimension 1 by (dim+1); these /// are produced by the balanced-cmvn code when it computes an offset and /// represents it as "fake stats". void ApplyCmvn(const MatrixBase &stats, bool norm_vars, MatrixBase *feats); } // namespace kaldi #endif // KALDI_TRANSFORM_CMVN_H_