Search Results for author: Brian MacWhinney

Found 10 papers, 1 papers with code

Evaluating Picture Description Speech for Dementia Detection using Image-text Alignment

no code implementations11 Aug 2023 Youxiang Zhu, Nana Lin, Xiaohui Liang, John A. Batsis, Robert M. Roth, Brian MacWhinney

We observe the difference between dementia and healthy samples in terms of the text's relevance to the picture and the focused area of the picture.

A New Benchmark of Aphasia Speech Recognition and Detection Based on E-Branchformer and Multi-task Learning

2 code implementations19 May 2023 Jiyang Tang, William Chen, Xuankai Chang, Shinji Watanabe, Brian MacWhinney

Our system achieves state-of-the-art speaker-level detection accuracy (97. 3%), and a relative WER reduction of 11% for moderate Aphasia patients.

Multi-Task Learning speech-recognition +1

Multilingual Alzheimer's Dementia Recognition through Spontaneous Speech: a Signal Processing Grand Challenge

no code implementations13 Jan 2023 Saturnino Luz, Fasih Haider, Davida Fromm, Ioulietta Lazarou, Ioannis Kompatsiaris, Brian MacWhinney

This Signal Processing Grand Challenge (SPGC) targets a difficult automatic prediction problem of societal and medical relevance, namely, the detection of Alzheimer's Dementia (AD).

Detecting cognitive decline using speech only: The ADReSSo Challenge

no code implementations23 Mar 2021 Saturnino Luz, Fasih Haider, Sofia de la Fuente, Davida Fromm, Brian MacWhinney

Building on the success of the ADReSS Challenge at Interspeech 2020, which attracted the participation of 34 teams from across the world, the ADReSSo Challenge targets three difficult automatic prediction problems of societal and medical relevance, namely: detection of Alzheimer's Dementia, inference of cognitive testing scores, and prediction of cognitive decline.

General Classification regression

Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge

no code implementations14 Apr 2020 Saturnino Luz, Fasih Haider, Sofia de la Fuente, Davida Fromm, Brian MacWhinney

ADReSS provides researchers with a benchmark speech dataset which has been acoustically pre-processed and balanced in terms of age and gender, defining two cognitive assessment tasks, namely: the Alzheimer's speech classification task and the neuropsychological score regression task.

Classification General Classification +1

Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning

no code implementations ACL 2016 Yulia Tsvetkov, Manaal Faruqui, Wang Ling, Brian MacWhinney, Chris Dyer

We use Bayesian optimization to learn curricula for word representation learning, optimizing performance on downstream tasks that depend on the learned representations as features.

Bayesian Optimization Representation Learning

Resources for the Detection of Conventionalized Metaphors in Four Languages

no code implementations LREC 2014 Lori Levin, Teruko Mitamura, Brian MacWhinney, Davida Fromm, Jaime Carbonell, Weston Feely, Robert Frederking, Anatole Gershman, Carlos Ramirez

The extraction rules operate on the output of a dependency parser and identify the grammatical configurations (such as a verb with a prepositional phrase complement) that are likely to contain conventional metaphors.

Two Approaches to Metaphor Detection

no code implementations LREC 2014 Brian MacWhinney, Davida Fromm

Methods for automatic detection and interpretation of metaphors have focused on analysis and utilization of the ways in which metaphors violate selectional preferences (Martin, 2006).

Vocal Bursts Valence Prediction

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