Improving Segment-based Speech Recognition by Recovering Missing Segments in Segment Graphs - A Thai Case Study

In segment-based speech recognition systems, the quality of the segmentation step is a major factor highly affecting their accuracies. This paper proposes methods to reduce missing segments caused by boundary insertion errors in segment graphs, which, in the case of Thai, could be generated from a probabilistic segmentation with limited speech resources. Acoustic discontinuities and manners of articulation are used to verify boundaries of the segment graph. Segments are added to the graph in the case of possible falsely detected boundaries. With the proposed insertion error eliminations, the best phonetic recognition accuracy achieved shows a 13.66% error reduction.