We describe a series of experiments in which memorybased machine learning techniques are used for the interpretation of spoken user input in human-machine interactions. In these experiments, the task is to determine the dialogue act of the user input and the type of information slots the user fills, on the basis of a variety of features representing the spoken input (speech measurements and wor...

Schizophrenic (n = 21), bipolar (n = 18), and normal control subjects (n = 21) were compared on a word recognition measure of semantic priming. The task involved the presentation of related, neutral, and unrelated word pairs; the second word (target word) in each pair was presented in a degraded form. Facilitation was defined as the accuracy of target word recognition for the related word pairs...

In this paper we describe the linguistic processor of a spoken dialogue system. The parser receives a word graph from the recognition module as its input. Its task is to find the best path through the graph. If no complete solution can be found, a robust mechanism for selecting multiple partial results is applied. We show how the information content rate of the results can be improved if the se...

Sixty-one noncredible patients (as documented by psychometric and behavioral criteria) scored significantly below 25 controls and 88 credible clinic patients with no motive to feign on most RAVLT scores, including added indices of implicit and "automatic" memory. A combination of true recognition (i.e., recognition minus false positives) + implicit memory score (i.e., the number of word stems c...

This paper proposes an improved method of summarizing speech in which a con dence measure of a word hypothesis is incorporated in the summarization score and also proposes a new method for evaluating the summarized sentences. The automatically summarized sentences were evaluated based on the precision of extracted keywords and each word string with a certain length in the manual summarizations ...

This paper proposes a new formulation for speech recognition/understanding systems, in which the a posteriori probability of a speaker’s message that the speaker intend to address given an observed acoustic sequence is maximized. This is an extension of the current criterion that maximizes a probability of a word sequence. Among the various possible representations, we employ cooccurrence score...

This paper presents a novel unsupervised calibration framework of word confidence measures for automatic speech recognition. It makes it possible to improve the quality of confidence measures in situations where the training of parametric models is hindered by a lack of human-labeled in-domain data. The proposed method calibrates confidence scores by utilizing recognition results stored in depl...

In this paper, we present MayoNLP’s results from the participation in the ScienceIE share task at SemEval 2017. We focused on the keyphrase classification task (Subtask B). We explored semantic similarities and patterns of keyphrases in scientific publications using pre-trained word embedding models. Word Embedding Distance Pattern, which uses the head noun word embedding to generate distance p...

The cognitive model of reading comprehension (RC) posits that RC is a result of the interaction between decoding and linguistic comprehension. Recently, the notion of decoding skill was expanded to include word recognition. In addition, some studies suggest that other skills could be integrated into this model, like processing speed, and have consistently indicated that this skill influences an...