2019/01/30 by Janneke van de Loo, Jort F. Gemmeke, van de Loo, Janneke +9 · 1 citation
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Speech Recognition and Synthesis #Speech and dialogue systems
paper · pdf · doi:10.48550/arxiv.1901.10680
openalex publication_date 2019/01/30 · openalex created_date 2022/07/30 · openalex updated_date 2026/07/28
We present a framework for the induction of semantic frames from utterances\nin the context of an adaptive command-and-control interface. The system is\ntrained on an individual user's utterances and the corresponding semantic\nframes representing controls. During training, no prior information on the\nalignment between utterance segments and frame slots and values is available.\nIn addition, semantic frames in the training data can contain information that\nis not expressed in the utterances. To tackle this weakly supervised\nclassification task, we propose a framework based on Hidden Markov Models\n(HMMs). Structural modifications, resulting in a hierarchical HMM, and an\nextension called expression sharing are introduced to minimize the amount of\ntraining time and effort required for the user.\n The dataset used for the present study is PATCOR, which contains commands\nuttered in the context of a vocally guided card game, Patience. Experiments\nwere carried out on orthographic and phonetic transcriptions of commands,\nsegmented on different levels of n-gram granularity. The experimental results\nshow positive effects of all the studied system extensions, with some effect\ndifferences between the different input representations. Moreover, evaluation\nexperiments on held-out data with the optimal system configuration show that\nthe extended system is able to achieve high accuracies with relatively small\namounts of training data.\n