565 citations · 990 across the 29 of their papers we have counts for
32 papers
Factors Affecting the Performance of Automated Speaker Verification in Alzheimer's Disease Clinical Trials
Malikeh Ehghaghi, Marija Stanojevic, Ali Akram +1
Detecting duplicate patient participation in clinical trials is a major challenge because repeated patients can undermine the credibility and accuracy of the trial's findings and r…
DEPAC: a Corpus for Depression and Anxiety Detection from Speech
Mashrura Tasnim, Malikeh Ehghaghi, Brian Diep +1
Mental distress like depression and anxiety contribute to the largest proportion of the global burden of diseases. Automated diagnosis systems of such disorders, empowered by recen…
Cost-effective Models for Detecting Depression from Speech
Mashrura Tasnim, Jekaterina Novikova
Depression is the most common psychological disorder and is considered as a leading cause of disability and suicide worldwide. An automated system capable of detecting signs of dep…
Multi-modal deep learning system for depression and anxiety detection
Brian Diep, Marija Stanojevic, Jekaterina Novikova
Traditional screening practices for anxiety and depression pose an impediment to monitoring and treating these conditions effectively. However, recent advances in NLP and speech mo…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
Data-driven Approach to Differentiating between Depression and Dementia from Noisy Speech and Language Data
Malikeh Ehghaghi, Frank Rudzicz, Jekaterina Novikova
A significant number of studies apply acoustic and linguistic characteristics of human speech as prominent markers of dementia and depression. However, studies on discriminating de…
DECK: Behavioral Tests to Improve Interpretability and Generalizability of BERT Models Detecting Depression from Text
Jekaterina Novikova, Ksenia Shkaruta
Models that accurately detect depression from text are important tools for addressing the post-pandemic mental health crisis. BERT-based classifiers' promising performance and the…
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran +74
Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
Impact of Environmental Noise on Alzheimer's Disease Detection from Speech: Should You Let a Baby Cry?
Jekaterina Novikova
Research related to automatically detecting Alzheimer's disease (AD) is important, given the high prevalence of AD and the high cost of traditional methods. Since AD significantly…
Robustness and Sensitivity of BERT Models Predicting Alzheimer's Disease from Text
Jekaterina Novikova
Understanding robustness and sensitivity of BERT models predicting Alzheimer's disease from text is important for both developing better classification models and for understanding…
Comparing Acoustic-based Approaches for Alzheimer's Disease Detection
Aparna Balagopalan, Jekaterina Novikova
Robust strategies for Alzheimer's disease (AD) detection are important, given the high prevalence of AD. In this paper, we study the performance and generalizability of three appro…
Augmenting BERT Carefully with Underrepresented Linguistic Features
Aparna Balagopalan, Jekaterina Novikova
Fine-tuned Bidirectional Encoder Representations from Transformers (BERT)-based sequence classification models have proven to be effective for detecting Alzheimer's Disease (AD) fr…
Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approach
Benjamin Eyre, Aparna Balagopalan, Jekaterina Novikova
Despite the widely reported success of embedding-based machine learning methods on natural language processing tasks, the use of more easily interpreted engineered features remains…
To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection
Aparna Balagopalan, Benjamin Eyre, Frank Rudzicz +1
Research related to automatically detecting Alzheimer's disease (AD) is important, given the high prevalence of AD and the high cost of traditional methods. Since AD significantly…
Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation
Aparna Balagopalan, Jekaterina Novikova, Matthew B. A. McDermott +3
Multi-language speech datasets are scarce and often have small sample sizes in the medical domain. Robust transfer of linguistic features across languages could improve rates of ea…
Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power
Jekaterina Novikova, Aparna Balagopalan, Ksenia Shkaruta +1
Understanding the vulnerability of linguistic features extracted from noisy text is important for both developing better health text classification models and for interpreting vuln…
Variations on the Chebyshev-Lagrange Activation Function
Yuchen Li, Frank Rudzicz, Jekaterina Novikova
We seek to improve the data efficiency of neural networks and present novel implementations of parameterized piece-wise polynomial activation functions. The parameters are the y-co…
Impact of ASR on Alzheimer's Disease Detection: All Errors are Equal, but Deletions are More Equal than Others
Aparna Balagopalan, Ksenia Shkaruta, Jekaterina Novikova
Automatic Speech Recognition (ASR) is a critical component of any fully-automated speech-based dementia detection model. However, despite years of speech recognition research, litt…
Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge
Ondřej Dušek, Jekaterina Novikova, Verena Rieser
This paper provides a comprehensive analysis of the first shared task on End-to-End Natural Language Generation (NLG) and identifies avenues for future research based on the result…
The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech
Aparna Balagopalan, Jekaterina Novikova, Frank Rudzicz +1
Speech datasets for identifying Alzheimer's disease (AD) are generally restricted to participants performing a single task, e.g. describing an image shown to them. As a result, mod…
Findings of the E2E NLG Challenge
Ondřej Dušek, Jekaterina Novikova, Verena Rieser
This paper summarises the experimental setup and results of the first shared task on end-to-end (E2E) natural language generation (NLG) in spoken dialogue systems. Recent end-to-en…
Detecting cognitive impairments by agreeing on interpretations of linguistic features
Zining Zhu, Jekaterina Novikova, Frank Rudzicz
Linguistic features have shown promising applications for detecting various cognitive impairments. To improve detection accuracies, increasing the amount of data or the number of l…
Deconfounding age effects with fair representation learning when assessing dementia
Zining Zhu, Jekaterina Novikova, Frank Rudzicz
One of the most prevalent symptoms among the elderly population, dementia, can be detected by classifiers trained on linguistic features extracted from narrative transcripts. Howev…