Two-dimensional frame-and-feature weighted Viterbi decoding for robust speech recognition

In this paper we propose a new approach of two-dimensional frame-and-feature weighted Viterbi decoding performed at the recognizer back-end for robust speech recognition. A new SVM-based frame weighting approach is proposed considering the energy distribution and harmonicity of the frame. The feature weighting is based on a previously proposed approach using an entropy measure considering confusion between phoneme classes. These two different weighting schemes on the two different dimensions are then properly integrated in Viterbi decoding in this paper. Extensive experiments performed with the Aurora 4 testing environment showed significant improvements.