The optimal distance measure for a given discrimination task under the nearest neighbor framework has been shown to be the likelihood that a pair of measurements have different class labels [5]. For implementation and efficiency considerations, the optimal distance measure was approximated by combining more elementary distance measures defined on simple feature spaces. In this paper, we address...

ROVER [1] and its successor voting procedures have been shown to be quite effective in reducing the recognition word error rate (WER). The success of these methods has been attributed to their minimum Bayes-risk (MBR) nature: they produce the hypothesis with the least expected word error. In this paper we develop a general procedure within the MBR framework, called segmental MBR recognition, th...

This paper proposes a new discriminative training method, called minimum sample risk (MSR), of estimating parameters of language models for text input. While most existing discriminative training methods use a loss function that can be optimized easily but approaches only approximately to the objective of minimum error rate, MSR minimizes the training error directly using a heuristic training p...

This paper proposes a new discriminative training method, called minimum sample risk (MSR), of estimating parameters of language models for text input. While most existing discriminative training methods use a loss function that can be optimized easily but approaches only approximately to the objective of minimum error rate, MSR minimizes the training error directly using a heuristic training p...