Usage Patterns and Latent Semantic Analyses for Task Goal Inference of Multimodal User Interactions

摘要

This paper describes our work in usage pattern analysis and development of a latent semantic analysis framework for interpreting multimodal user input consisting speech and pen gestures. We have designed and collected a multimodal corpus of navigational inquiries. Each modality carries semantics related to domain-specific task goal. Each inquiry is annotated manually with a task goal based on the semantics. Multimodal input usually has a simpler syntactic structure than unimodal input and the order of semantic constituents is different in multimodal and unimodal inputs. Therefore, we proposed to use semantic analysis to derive the latent semantics from the multimodal inputs using latent semantic modeling (LSM). In order to achieve this, we parse the recognized Chinese spoken input for the spoken locative references (SLR). These SLRs are then aligned with their corresponding pen gesture(s). Then, we characterized the cross-modal integration pattern as 3-tuple multimodal terms with SLR, pen gesture type and their temporal relation. The inquiry-multimodal term matrix is then decomposed using singular value decomposition (SVD) to derive the latent semantics automatically. Task goal inference based on the latent semantics shows that the task goal inference accuracy on a disjoint test set is of 99%.