Papers by Sakriani Sakti

7 papers
Refining rtMRI Landmark-Based Vocal Tract Contour Labels with FCN-Based Smoothing and Point-to-Curve Projection (2024.lrec-main)

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Challenge: Existing methods for labeling articulator contours are based on a ground truth label, but there are occasional errors.
Approach: They propose to refine landmark-based vocal-tract contour labels using outlier removal, full convolutional network and a landmark point-to-edge curve projection technique.
Outcome: The proposed labels outperform existing labels and their accuracy through subjective assessments of several contour areas.
Speech Recognition and Meaning Interpretation: Towards Disambiguation of Structurally Ambiguous Spoken Utterances in Indonesian (2023.emnlp-main)

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Challenge: Ambiguity is one of the challenges in natural language processing.
Approach: They propose to resolve structurally ambiguous sentences into unambiguous texts in Indonesian using prosodic information.
Outcome: The proposed system achieves a disambiguation accuracy of 79.6% while the proposed direct system yields an even more impressive disambiguations accuracy of 82%.
Construction of English-French Multimodal Affective Conversational Corpus from TV Dramas (L18-1)

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Challenge: Existing technologies for speech recognition and speech synthesis focus on non-verbal content and paralinguistic information.
Approach: They propose to construct a multimodal affective conversational corpus based on TV dramas . their data contain parallel English-French languages in lexical, acoustic, and facial features .
Outcome: The proposed corpus can be used to assess speech recognition, speech recognition and synthesis, linguistic, and paralinguistic speech-to-speech translation and multimodal dialog systems.
NusaCrowd: Open Source Initiative for Indonesian NLP Resources (2023.findings-acl)

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Challenge: Existing NLP research in Indonesian languages has been held back by factors such as language diversity, orthographic variation, resource limitation and other societal challenges.
Approach: They present a collaborative initiative to collect and unify existing resources for Indonesian languages and open access to previously non-public resources.
Outcome: The results show that the datasets are highly reliable and can be used to generate the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia.
Emotional Speech Corpus for Persuasive Dialogue System (2020.lrec-1)

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Challenge: Emotional expressions can be used to express the speaker’s emotion more directly than using only emotion expression in the text.
Approach: They built a speech dialogue corpus in a persuasive scenario that uses emotional expressions to build a system with emotional expression.
Outcome: The proposed system can express the speaker's emotion more directly than using only emotion expression in the text, and the results show that the collected emotional expressions with their speeches have higher emotional expressiveness for expressing the system's emotions to users.
Dialogue Scenario Collection of Persuasive Dialogue with Emotional Expressions via Crowdsourcing (L18-1)

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Challenge: Existing methods for data collection and annotation are costly and prevent launching new dialogue systems.
Approach: They asked crowd workers to create persuasive dialogue systems using emotional expressions . they annotated emotional states and users' acceptance for system persuasion .
Outcome: The proposed system has sufficient agreement even without training, the researchers found . the experiment showed that the collected data are comparable to real-world dialogue recording methods .
Automatic Generation of a Compositional QA Benchmark for Geospatial Reasoning under Spatial and Entity Constraints (2026.eacl-srw)

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Challenge: Recent advances in large language models have enhanced their ability to perform reasoning tasks that integrate linguistic, visual, and factual information.
Approach: They propose a method for constructing compositional geographic question answering datasets that jointly consider spatial and entity constraints.
Outcome: The proposed method performs well on questions involving rich entity grounding, but its accuracy drops on quantitative spatial reasoning questions.

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