arXiv subjects

Daniel Blackburn

Publications and source records attributed to Daniel Blackburn.

6 recordsLinked to original sources

arXiv2026

PROCESS-2: A Benchmark Speech Corpus for Early Cognitive Impairment Detection

Speech-based analysis offers a scalable and non-invasive approach for detecting cognitive decline, yet progress has been constrained by the limited availability of clinically validated datasets collected under realistic conditions. We introduce PROCESS-2, a large-scale speech dataset designed to support research on automatic assessment of cognitive impairment from spontaneous and task-oriented speech. The dataset comprises recordings from 200 healthy controls, 150 mild cognitive impairment, and 50 dementia diagnoses collected using the CognoMemory digital assessment platform. Each participant completed a single assessment session, including picture description and verbal fluency tasks, accompanied by manually verified transcripts and participant-level metadata. PROCESS-2 contains approximately 21 hours of speech audio with predefined train/test partitions. Comprehensive technical validation evaluated demographic balance, clinical consistency, recording stability, embedding-space structure, and reproducible baseline modelling performance, demonstrating clinically meaningful group separation and stable performance across modelling approaches while preserving real-world conversational variability. PROCESS-2 is released under controlled access via Hugging Face to enable responsible reuse while protecting participant privacy, providing a reproducible benchmark resource for speech-based cognitive assessment research.

cs.SD↗

arXiv2026

False positive bias in AI-powered speech-based cognitive screening for multilingual English speakers in the UK

Conversational speech reveals early signs of cognitive decline, including dementia and mild cognitive impairment (MCI). AI models show promise for speech-based screening, yet most research focuses on monolingual groups. In the UK, dementia is projected to rise fastest among Black and Asian communities, where multilingualism is common, making equity assessment critical. We recruited 1,395 participants (monolingual English speakers and multilingual speakers from Sheffield/Bradford) and collected over 263 hours of speech via the CognoMemory agent. Multilingual participants spoke English alongside Somali, Chinese, or South Asian languages (Hindi, Urdu, Punjabi, Mirpuri, Arabic). We evaluated ASR (Whisper, Wav2Vec 2.0, NeMo) and downstream AI models for cognitive classification and MMSE regression. ASR accuracy showed no significant differences across groups. However, downstream models exhibited systematic disparities: multilingual speakers were more often misclassified as impaired, especially in memory, fluency, and reading tasks. False-positive rates were substantially higher for multilingual (28 to 37%) than monolingual (12 to 16%) speakers, meaning multilingual individuals were approximately 2.5 times more likely to receive incorrect impairment labels. These biases worsened when models were trained on DementiaBank. This is the first large-scale analysis of false-positive bias in speech-based AI cognitive screening for UK multilingual ethnic minorities. Despite strong overall performance, current models show measurable disparities affecting multilingual speakers. Addressing these biases is essential for safe, equitable deployment in diverse healthcare settings.

cs.CL↗

arXiv2025

CognoSpeak: an automatic, remote assessment of early cognitive decline in real-world conversational speech

The early signs of cognitive decline are often noticeable in conversational speech, and identifying those signs is crucial in dealing with later and more serious stages of neurodegenerative diseases. Clinical detection is costly and time-consuming and although there has been recent progress in the automatic detection of speech-based cues, those systems are trained on relatively small databases, lacking detailed metadata and demographic information. This paper presents CognoSpeak and its associated data collection efforts. CognoSpeak asks memory-probing long and short-term questions and administers standard cognitive tasks such as verbal and semantic fluency and picture description using a virtual agent on a mobile or web platform. In addition, it collects multimodal data such as audio and video along with a rich set of metadata from primary and secondary care, memory clinics and remote settings like people's homes. Here, we present results from 126 subjects whose audio was manually transcribed. Several classic classifiers, as well as large language model-based classifiers, have been investigated and evaluated across the different types of prompts. We demonstrate a high level of performance; in particular, we achieved an F1-score of 0.873 using a DistilBERT model to discriminate people with cognitive impairment (dementia and people with mild cognitive impairment (MCI)) from healthy volunteers using the memory responses, fluency tasks and cookie theft picture description. CognoSpeak is an automatic, remote, low-cost, repeatable, non-invasive and less stressful alternative to existing clinical cognitive assessments.

cs.SD↗

arXiv2024

Early Dementia Detection Using Multiple Spontaneous Speech Prompts: The PROCESS Challenge

Dementia is associated with various cognitive impairments and typically manifests only after significant progression, making intervention at this stage often ineffective. To address this issue, the Prediction and Recognition of Cognitive Decline through Spontaneous Speech (PROCESS) Signal Processing Grand Challenge invites participants to focus on early-stage dementia detection. We provide a new spontaneous speech corpus for this challenge. This corpus includes answers from three prompts designed by neurologists to better capture the cognition of speakers. Our baseline models achieved an F1-score of 55.0% on the classification task and an RMSE of 2.98 on the regression task.

cs.SD↗

arXiv2020

Data augmentation using generative networks to identify dementia

Data limitation is one of the most common issues in training machine learning classifiers for medical applications. Due to ethical concerns and data privacy, the number of people that can be recruited to such experiments is generally smaller than the number of participants contributing to non-healthcare datasets. Recent research showed that generative models can be used as an effective approach for data augmentation, which can ultimately help to train more robust classifiers sparse data domains. A number of studies proved that this data augmentation technique works for image and audio data sets. In this paper, we investigate the application of a similar approach to different types of speech and audio-based features extracted from interactions recorded with our automatic dementia detection system. Using two generative models we show how the generated synthesized samples can improve the performance of a DNN based classifier. The variational autoencoder increased the F-score of a four-way classifier distinguishing the typical patient groups seen in memory clinics from 58% to around 74%, a 16% improvement

eess.AS↗

arXiv2019

Detecting Alzheimer's Disease by estimating attention and elicitation path through the alignment of spoken picture descriptions with the picture prompt

Cognitive decline is a sign of Alzheimer's disease (AD), and there is evidence that tracking a person's eye movement, using eye tracking devices, can be used for the automatic identification of early signs of cognitive decline. However, such devices are expensive and may not be easy-to-use for people with cognitive problems. In this paper, we present a new way of capturing similar visual features, by using the speech of people describing the Cookie Theft picture - a common cognitive testing task - to identify regions in the picture prompt that will have caught the speaker's attention and elicited their speech. After aligning the automatically recognised words with different regions of the picture prompt, we extract information inspired by eye tracking metrics such as coordinates of the area of interests (AOI)s, time spent in AOI, time to reach the AOI, and the number of AOI visits. Using the DementiaBank dataset we train a binary classifier (AD vs. healthy control) using 10-fold cross-validation and achieve an 80% F1-score using the timing information from the forced alignments of the automatic speech recogniser (ASR); this achieved around 72% using the timing information from the ASR outputs.

cs.CL↗