Interruption point detection of spontaneous speech using prior knowledge and multiple features

This paper presents an approach to interruption point (IP) detection of spontaneous speech based on conditional random fields using prior knowledge and multiple features. The features adopted in this study consist of subsyllable boundaries and prosodic features. Conditional random fields (CRFs) and variable-length contextual features are employed for IP modeling. In order to apply the features with continuous values to the CRF models, the K-means clustering algorithm is adopted for the quantization of the prosodic features. In the experimental results, Mandarin Conversional Dialogue Corpus (MCDC) was used to evaluate the proposed method. The IP detection error rate achieved almost 20% reduction in Rt04 measure. The experimental results show that the proposed model can effectively detect the interruption point in spontaneous speech.