This paper presents a novel method for reducing the dimensionality of kernel spaces. Recently, to maintain the convexity of training, loglinear models without mixtures have been u...
This paper presents a semantic confidence measure that aims to predict the relevance of automatic transcripts for a task of Spoken Document Retrieval (SDR). The proposed predicti...
Varying channel conditions present a difficult problem for many speech technologies such as language identification (LID). Channel compensation techniques have been shown to sig...
In the Weighted Finite State Transducer (WFST) framework for speech recognition, we can reduce memory usage and increase flexibility by using on-the-fly composition which genera...
Tasuku Oonishi, Paul R. Dixon, Koji Iwano, Sadaoki...
In this paper we investigate a discriminative approach to feature weighting for topic identification using minimum classification error (MCE) training. Our approach learns featu...