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On Reinforcement Learning, Effect Handlers, and the State Monad ...
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122 |
On the Importance of Data Size in Probing Fine-tuned Models ...
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125 |
CTA-RNN: Channel and Temporal-wise Attention RNN Leveraging Pre-trained ASR Embeddings for Speech Emotion Recognition ...
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Fine-grained Noise Control for Multispeaker Speech Synthesis ...
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127 |
Emotion Intensity and its Control for Emotional Voice Conversion ...
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128 |
Dawn of the transformer era in speech emotion recognition: closing the valence gap ...
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129 |
Suum Cuique: Studying Bias in Taboo Detection with a Community Perspective ...
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130 |
Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Maps ...
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132 |
An Evaluation Dataset for Legal Word Embedding: A Case Study On Chinese Codex ...
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133 |
Classifying Autism from Crowdsourced Semi-Structured Speech Recordings: A Machine Learning Approach ...
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Abstract:
Autism spectrum disorder (ASD) is a neurodevelopmental disorder which results in altered behavior, social development, and communication patterns. In past years, autism prevalence has tripled, with 1 in 54 children now affected. Given that traditional diagnosis is a lengthy, labor-intensive process, significant attention has been given to developing systems that automatically screen for autism. Prosody abnormalities are among the clearest signs of autism, with affected children displaying speech idiosyncrasies including echolalia, monotonous intonation, atypical pitch, and irregular linguistic stress patterns. In this work, we present a suite of machine learning approaches to detect autism in self-recorded speech audio captured from autistic and neurotypical (NT) children in home environments. We consider three methods to detect autism in child speech: first, Random Forests trained on extracted audio features (including Mel-frequency cepstral coefficients); second, convolutional neural networks (CNNs) ... : 17 pages, 4 figures, submitted to JMIR Pediatrics and Parenting ...
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Keyword:
Audio and Speech Processing eess.AS; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG; Sound cs.SD
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URL: https://dx.doi.org/10.48550/arxiv.2201.00927 https://arxiv.org/abs/2201.00927
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134 |
Application of Quantum Density Matrix in Classical Question Answering and Classical Image Classification ...
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135 |
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense ...
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136 |
Detecting early signs of depression in the conversational domain: The role of transfer learning in low-resource scenarios ...
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137 |
LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback ...
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138 |
Counterfactual Explanations for Natural Language Interfaces ...
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Probing Speech Emotion Recognition Transformers for Linguistic Knowledge ...
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