Dynamic-Programming–Based Method for Fixation-to-Word Mapping

Yamaya, Akito; Topić, Goran; Martínez-Gómez, Pascual; Aizawa, Akiko

doi:10.1007/978-3-319-19857-6_55

Cited by 10 publications

(15 citation statements)

References 4 publications

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“…The cost function is based on the length of reading segments and document text lines to determine whether a reading line matches a document line. Our experiments demonstrate that the proposed method, as a preparatory step, yields 87% accuracy in naïve distance-based fixation-to-word mapping (up from 69% of pure naïve method, and 72.3% of the previous method without transition classification [16]). Based on this result, we believe our method to be a potentially valuable instrument in the processing of gaze data from reading activities.…”

Section: Resultsmentioning

confidence: 89%

“…Although this method achieved feasible accuracy, the precision of the word-level error correction was relatively less. Our previous study [16] considered sequential consecutive fixations, inspired by Carl's [3] dynamic programming-based approach, and associated sequential fixations with a text line to reduce the vertical misplacement of the gaze position. Although our previous study obtained better accuracy compared with manual annotation, it may not perform well in the presence of long-range regressions or skimming events.…”

Section: Related Workmentioning

confidence: 99%

“…We extended both the similarity function and the penalty score of our previous study [16] to enhance the accuracy and robustness.…”

Section: Segment-to-line Mappingmentioning

confidence: 99%

“…Baseline is the naïve mapping produced by each fixation immediately shifting to the closest word. DP uses the dynamic programming-based segmentto-line mapping (described in our previous work [16]) as a prestep to the naïve mapping. DP+Classifier uses our proposed mapping technique with transition classification as a pre-step to naïve mapping.…”

Section: Experiments 2: Accuracy Of Fixation-to-word Mappingmentioning

confidence: 99%

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Vertical Error Correction Using Classification of Transitions between Sequential Reading Segments

Yamaya

Topić

Aizawa

2017

Journal of Information Processing

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Abstract:In eye-tracking-based reading behavior research, gaze sampling errors often negatively affect gaze-to-word mapping. In this paper, we propose a method for more accurate mapping by first taking adjacent horizontally progressive fixations as segments, and then classifying the segments into six classes using a random forest classifier. The segments are then reconstructed based on the classification, and are associated with a document line using a dynamic programming algorithm. The combination of segment-to-line mapping and transition classification achieved 87% mapping accuracy. We also witnessed a reduction of manual annotation time when the mapping was used as an annotation guiding tool.

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Section: Resultsmentioning

confidence: 89%

Section: Related Workmentioning

confidence: 99%

“…We extended both the similarity function and the penalty score of our previous study [16] to enhance the accuracy and robustness.…”

Section: Segment-to-line Mappingmentioning

confidence: 99%

Section: Experiments 2: Accuracy Of Fixation-to-word Mappingmentioning

confidence: 99%

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Vertical Error Correction Using Classification of Transitions between Sequential Reading Segments

Yamaya

Topić

Aizawa

2017

Journal of Information Processing

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“…Then, the vertical error is coarsely estimated and the fixation lines are matched with the text lines. In the next section, we present the algorithm proposed by Yamaya et al [15] for correcting the vertical error and compare it to our method. After this, we show through the experiments that we can align 69 % of the eye gaze lines with their corresponding text lines and compare the results with the state-of-the-art method.…”

Section: Introductionmentioning

confidence: 99%

Vertical error correction of eye trackers in nonrestrictive reading condition

Sanches

Augereau

Kise

2016

IPSJ T Comput Vis Appl

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The eye tracking technology is used for four decades for studying reading behavior. The applications are various: estimating the reader comprehension, identifying the reader, summarizing a read document, creating a reading-life log, etc. The gaze data used in such applications has to be accurate enough to perform the analysis. In order to improve the accuracy, most of the experiments are set up with restrictive conditions such as using a head fixation and a professional eye tracker. It implies that the results are valid only in restrictive laboratory settings and an unrealistic small error is produced by the experiment. However, the use of affordable eye trackers in realistic conditions of reading leads to large errors in the recordings. We propose a new algorithm to correct the vertical error and to align the gazes with the text. The proposed algorithm is robust to rereading and skipping some parts of text, contrary to all the other algorithms of the state of the art. We show that up to 69 % of the gazes are aligned with the correct text lines.

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Eye Got It: A System for Automatic Calculation of the Eye-Voice Span

Baha

Augereau

Kobylyanskaya

et al. 2022

Lecture Notes in Computer Science

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Over the past decade, eye movement has been widely looked into for describing and analyzing several cognitive processes and especially for human-document interaction, such as estimating reading ability and document understanding. Most of the existing applications have been done for silent reading but we propose to explore reading aloud interaction through a powerful measurement named the "eye-voice span" which measures the distance between the eyes and the voice. In this paper we present an open-source platform named "Eye got it" and the underlying algorithms that can be used for processing eye-tracking and voice data in order to compute automatically the eye-voice span.

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Dynamic-Programming–Based Method for Fixation-to-Word Mapping

Cited by 10 publications

References 4 publications

Vertical Error Correction Using Classification of Transitions between Sequential Reading Segments

Vertical Error Correction Using Classification of Transitions between Sequential Reading Segments

Vertical error correction of eye trackers in nonrestrictive reading condition

Eye Got It: A System for Automatic Calculation of the Eye-Voice Span

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