A Segmentation Posterior based Endpointing Algorithm - Xie, Yanlu; Shi, Yu; Soong, Frank; Dai, Beiqian (ICASSP 2007, 2007)
A segmentation posterior probability based endpointing algorithm for robust ASR is proposed. First, each speech signal is partitioned into homogeneous segments via auto-segmentation. Then posterior probabilities of all possible endpoints are computed, based on the segmentation likelihoods of all levels in a selected range. Endpoints with the highest posterior probabilities are finally selected. The new method differs from the previous auto-segmentation and clustering based algorithm on that the former considers hypotheses from several levels, while the latter depends only on one appropriate level. Another potential benefit of the proposed method is that any endpointing or VAD results can be integrated, as hypotheses, into the posterior probability framework. Experiments based on the AURORA2 digit database show the robustness of the proposed method.