Search Results for author:
Found 7 papers, 5 papers with code
Reexamining Racial Disparities in Automatic Speech Recognition Performance: The Role of Confounding by Provenance
Automatic speech recognition (ASR) models trained on large amounts of audio data are now widely used to convert speech to written text in a variety of applications from video captioning to automated assistants used in healthcare and other domains.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Language Models Resist Alignment
Empirically, we demonstrate the elasticity of post-alignment models, i. e., the tendency to revert to the behavior distribution formed during the pre-training phase upon further fine-tuning.
Too Big to Fail: Larger Language Models are Disproportionately Resilient to Induction of Dementia-Related Linguistic Anomalies
As artificial neural networks grow in complexity, understanding their inner workings becomes increasingly challenging, which is particularly important in healthcare applications.
Useful Blunders: Can Automated Speech Recognition Errors Improve Downstream Dementia Classification?
\textbf{Results}: Imperfect ASR-generated transcripts surprisingly outperformed manual transcription for distinguishing between individuals with AD and those without in the ``Cookie Theft'' task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
TRESTLE: Toolkit for Reproducible Execution of Speech, Text and Language Experiments
The evidence is growing that machine and deep learning methods can learn the subtle differences between the language produced by people with various forms of cognitive impairment such as dementia and cognitively healthy individuals.
The Far Side of Failure: Investigating the Impact of Speech Recognition Errors on Subsequent Dementia Classification
Linguistic anomalies detectable in spontaneous speech have shown promise for various clinical applications including screening for dementia and other forms of cognitive impairment.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
GPT-D: Inducing Dementia-related Linguistic Anomalies by Deliberate Degradation of Artificial Neural Language Models
Deep learning (DL) techniques involving fine-tuning large numbers of model parameters have delivered impressive performance on the task of discriminating between language produced by cognitively healthy individuals, and those with Alzheimer's disease (AD).
