Discriminative feature extraction for speech recognition using continuous output codes

Feature transformation techniques have been widely investigated to reduce feature redundancy and to introduce additional discriminative information with the aim to improve the performance of automatic speech recognition (ASR). In this paper, we propose a novel method to obtain discriminative feature transformation based on output coding technique for speech recognition. The output coding transformation projects the speech features from their original space to a new one where each dimension of the features captures information to distinguish different phones. Using polynomial expansion, the short-time spectral features are first expanded to a high-dimensional space where the generalized linear discriminant sequence kernel is applied on the sequences of input feature vectors. Then, the output coding transformation formulated via a set of linear SVMs projects the sequences of high dimensional vectors into a tractable low-dimensional feature space where the resultant features are well-separated continuous output codes for the subsequent multi-class classification problem. Our experimental results on the TIMIT corpus show that the proposed features achieve 10.5% ASR error rate reduction over the conventional spectral features.

► We design discriminative feature transform using output coding for speech recognition. ► We employ SVMs with GLDS kernels in the output coding structure. ► We introduce a classifier selection scheme to find an effective set of output codes. ► Compared to MFCCs, generated output codes improve the performance more than 10%.