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1
ClassSPLOM -- A Scatterplot Matrix to Visualize Separation of Multiclass Multidimensional Data ...
Aupetit, Michael; Ali, Ahmed. - : arXiv, 2022
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2
Decoding Neural Correlation of Language-Specific Imagined Speech using EEG Signals ...
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3
TFS Recognition: Investigating MPH]{Thai Finger Spelling Recognition: Investigating MediaPipe Hands Potentials ...
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4
A Feasibility Study of Answer-Agnostic Question Generation for Education ...
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5
Neural Topic Modeling of Psychotherapy Sessions ...
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6
Reproducible Subjective Evaluation ...
Morrison, Max; Tang, Brian; Tan, Gefei. - : arXiv, 2022
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7
Captivate! Contextual Language Guidance for Parent-Child Interaction ...
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8
Rethinking Offensive Text Detection as a Multi-Hop Reasoning Problem ...
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9
AI-Based Automated Speech Therapy Tools for persons with Speech Sound Disorders: A Systematic Literature Review ...
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10
Cross-Platform Difference in Facebook and Text Messages Language Use: Illustrated by Depression Diagnosis ...
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11
Multi-Mosaics: Corpus Summarizing and Exploration using multiple Concordance Mosaic Visualisations ...
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12
Wavebender GAN: An architecture for phonetically meaningful speech manipulation ...
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13
Towards a Sociolinguistics-Based Framework for the Study of Politeness in Human-Computer Interaction ...
Bar-Or, Ella; Regev, Tom; Shaviv, Paz. - : arXiv, 2022
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14
Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction ...
Abstract: Speech emotion recognition systems have high prediction latency because of the high computational requirements for deep learning models and low generalizability mainly because of the poor reliability of emotional measurements across multiple corpora. To solve these problems, we present a speech emotion recognition system based on a reductionist approach of decomposing and analyzing syllable-level features. Mel-spectrogram of an audio stream is decomposed into syllable-level components, which are then analyzed to extract statistical features. The proposed method uses formant attention, noise-gate filtering, and rolling normalization contexts to increase feature processing speed and tolerance to adversity. A set of syllable-level formant features is extracted and fed into a single hidden layer neural network that makes predictions for each syllable as opposed to the conventional approach of using a sophisticated deep learner to make sentence-wide predictions. The syllable level predictions help to achieve the ... : 13 pages, 5 figures, currently under review by IEEE/ACM Transactions on Audio, Speech, and Language Processing ...
Keyword: Audio and Speech Processing eess.AS; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Human-Computer Interaction cs.HC; I.5.2; I.5.5; Machine Learning cs.LG; Sound cs.SD
URL: https://arxiv.org/abs/2204.11382
https://dx.doi.org/10.48550/arxiv.2204.11382
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15
A Short Introduction to Information-Theoretic Cost-Benefit Analysis ...
Chen, Min. - : arXiv, 2021
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16
Learning Activities in Colours and Rainbows for Programming Skill Development ...
Roberts, Jonathan C.. - : arXiv, 2021
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17
Black Feminist Musings on Algorithmic Oppression ...
Hampton, Lelia Marie. - : arXiv, 2021
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18
Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection ...
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19
Designing Building Blocks for Open-Ended Early Literacy Software ...
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20
Can we aggregate human intelligence? an approach for human centric aggregation using ordered weighted averaging operators ...
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