Unsupervisec cross-validation adaptation algorithms for improved adaptation performance

An unsupervised cross-validation adaptation algorithm and its variation are proposed that introduce the idea of cross-validation in the unsupervised batch-mode adaptation framework to improve the adaptation performance. The first algorithm is constructed on a general adaptation technique such as MLLR and can be used in combination with any adaptation method. The second algorithm is a modified version of the first algorithm and works with lower computational cost by assuming MLLR. These algorithms are extensions of our previously proposed CV training methods and are useful to suppress the negative effect of the conventional unsupervised batch-mode adaptation process that reinforces the errors included in automatic transcriptions. The proposed algorithms were evaluated in domain adaptation, speaker adaptation, and in their combination for large vocabulary spontaneous speech recognition. When the domain and speaker adaptations were combined using a read speech initial model, the relative word error rate reduction by the proposed method was 29% whereas the reduction by the conventional approach was 23%.