Knowledge-based Quadratic Discriminant Analysis for phonetic classification

Modeling the second-order statistics of articulatory trajectories is likely to improve the performance in classifying phone segments compared to using only linear combinations of MFCCs. Nevertheless, the extremely high dimensionality of the feature space spanned by a combination of monomials of degree-1 and degree-2 makes it difficult to effectively exploit the discriminative information in the full covariance matrix. This paper proposes a novel algorithm, dubbed Knowledge-based Quadratic Discriminant Analysis (KnQDA), for reducing the number of dimensions of the space spanned by degree-1 and degree-2 monomials by using phonetic knowledge for selecting the set of degree-2 monomials that are most likely to improve classification. KnQDA seeks a trade-off between overfitting and undertraining, which further improves the learnability. Binary classifications on all pairs of phones in TIMIT show the effectiveness of the proposed method, especially on those phone pairs that overlap strongly in the linear feature space.