Discriminative feature transformation using output coding for speech recognition

In this paper, we present a new mechanism to extract discriminative acoustic features for speech recognition using continuous output coding (COC) based feature transformation. Our proposed method first expands the short-time spectral features into a higher dimensional feature space to improve its discriminative capability. The expansion is performed by employing the polynomial expansion. The high dimension features are then projected into lower dimension space using continuous output coding technique implemented by a set of linear SVMs. The resulting feature vectors are designed to encode the difference between phones. The generated features are shown to be more discriminative than MFCCs and experimental results on both TIMIT and NTIMIT corpus showed better phone recognition accuracy with the proposed features.