717 research outputs found
Combining quantitative narrative analysis and predictive modeling - an eye tracking study
As a part of a larger interdisciplinary project on Shakespeare sonnets’ reception (Jacobs et al., 2017; Xue et al., 2017), the present study analyzed the eye movement behavior of participants reading three of the 154 sonnets as a function of seven lexical features extracted via Quantitative Narrative Analysis (QNA). Using a machine learning- based predictive modeling approach five ‘surface’ features (word length, orthographic neighborhood density, word frequency, orthographic dissimilarity and sonority score) were detected as important predictors of total reading time and fixation probability in poetry reading. The fact that one phonological feature, i.e., sonority score, also played a role is in line with current theorizing on poetry reading. Our approach opens new ways for future eye movement research on reading poetic texts and other complex literary materials (cf. Jacobs, 2015c)
Prosodic modules for speech recognition and understanding in VERBMOBIL
Within VERBMOBIL, a large project on spoken language research in Germany, two modules for detecting and recognizing prosodic events have been developed. One module operates on speech signal parameters and the word hypothesis graph, whereas the other module, designed for a novel, highly interactive architecture, only uses speech signal parameters as its input. Phrase boundaries, sentence modality, and accents are detected. The recognition rates in spontaneous dialogs are for accents up to 82,5%, for phrase boundaries up to 91,7%
Human-human, human-machine communication: on the HuComTech multimodal corpus
The present paper describes HuComTech, a multimodal corpus featuring over 50 hours of video taped interviews with 112 informants. The interviews were carried out in a lab equipped with multiple cameras and microphones able to record posture, hand gestures, facial expressions, gaze etc. as well as the acoustic and linguistic features of what was said. As a result of large-scale manual and semi-automatic annotation, the HuComTech corpus offers a rich dataset on 47 annotation levels. The paper presents the objectives, the workflow, the annotation work, focusing on two aspects in particular i.e. time alignment made with the Leipzig tool WEBMaus and the automatic detection of intonation contours developed by the HuComTech team. Early exploitation of the corpus included analysis of hidden patterns with the use of sophisticated multivariat analysis of temporal relations within the data points. The HuComTech corpus is one o the flagship language resources available through the HunCLARIN repositor
Language networks: Their structure, function, and evolution
Several important recent advances in various sciences (particularly biology and physics) are based
on complex network analysis, which provides tools for characterising statistical properties of networks
and explaining how they may arise. This article examines the relevance of this trend for the study of human languages. We review some early efforts to build up language networks, characterise
their properties, and show in which direction models are being developed to explain them. These insights are relevant, both for studying fundamental unsolved puzzles in cognitive science, in particular the origins and evolution of language, but also for recent data-driven statistical
approaches to natural language.This work has been supported by grants FIS2004-0542, IST-FET ECAGENTS project of the European Community founded under EU R&D contract 011940, IST-FET DELIS project under
EU R&D contract 001907, by the Santa Fe Institute and the Sony Computer Science Laboratory.N
Lexical Access Model for Italian -- Modeling human speech processing: identification of words in running speech toward lexical access based on the detection of landmarks and other acoustic cues to features
Modelling the process that a listener actuates in deriving the words intended
by a speaker requires setting a hypothesis on how lexical items are stored in
memory. This work aims at developing a system that imitates humans when
identifying words in running speech and, in this way, provide a framework to
better understand human speech processing. We build a speech recognizer for
Italian based on the principles of Stevens' model of Lexical Access in which
words are stored as hierarchical arrangements of distinctive features (Stevens,
K. N. (2002). "Toward a model for lexical access based on acoustic landmarks
and distinctive features," J. Acoust. Soc. Am., 111(4):1872-1891). Over the
past few decades, the Speech Communication Group at the Massachusetts Institute
of Technology (MIT) developed a speech recognition system for English based on
this approach. Italian will be the first language beyond English to be
explored; the extension to another language provides the opportunity to test
the hypothesis that words are represented in memory as a set of
hierarchically-arranged distinctive features, and reveal which of the
underlying mechanisms may have a language-independent nature. This paper also
introduces a new Lexical Access corpus, the LaMIT database, created and labeled
specifically for this work, that will be provided freely to the speech research
community. Future developments will test the hypothesis that specific acoustic
discontinuities - called landmarks - that serve as cues to features, are
language independent, while other cues may be language-dependent, with powerful
implications for understanding how the human brain recognizes speech.Comment: Submitted to Language and Speech, 202
Reading Shakespeare sonnets: Combining quantitative narrative analysis and predictive modeling - an eye tracking study
As a part of a larger interdisciplinary project on Shakespeare sonnets’ reception (Jacobs et al., 2017; Xue et al., 2017), the present study analyzed the eye movement behavior of participants reading three of the 154 sonnets as a function of seven lexical features extracted via Quantitative Narrative Analysis (QNA). Using a machine learning- based predictive modeling approach five ‘surface’ features (word length, orthographic neighborhood density, word frequency, orthographic dissimilarity and sonority score) were detected as important predictors of total reading time and fixation probability in poetry reading. The fact that one phonological feature, i.e., sonority score, also played a role is in line with current theorizing on poetry reading. Our approach opens new ways for future eye movement research on reading poetic texts and other complex literary materials (cf. Jacobs, 2015c)
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