Breaking Barriers: Exploring the Diagnostic Potential of Speech Narratives in H indi for A lzheimer’s Disease (2023)

Abstract: Alzheimer’s Disease (AD) is a neurodegenerative disorder that affects cognitive abilities and memory, especially in older adults. One of the challenges of AD is that it can be difficult to diagnose in its early stages. However, recent research has shown that changes in language, including speech decline and difficulty in processing information, can be important indicators of AD and may help with early detection. Hence, the speech narratives of the patients can be useful in diagnosing the early stages of Alzheimer’s disease. While the previous works have presented the potential of using speech narratives to diagnose AD in high-resource languages, this work explores the possibility of using a low-resourced language, i.e., Hindi language, to diagnose AD. In this paper, we present a dataset specifically for analyzing AD in the Hindi language, along with experimental results using various state-of-the-art algorithms to assess the diagnostic potential of speech narratives in Hindi. Our analysis suggests that speech narratives in the Hindi language have the potential to aid in the diagnosis of AD. Our dataset and code are made publicly available at https://github.com/rkritesh210/DementiaBankHindi .

## introduction alzheimer's disease (ad) is the most typical kind of dementia, characterized by a specific pattern of cognitive and functional deterioration brought on by aging that may eventually lead to death @xcite . this condition is mostly seen in adults over 60. @xcite predicted that by 2040, more than 80 million people would be affected by dementia globally, up from an estimated 24 million in 2001. in the early stages of ad, it is common to experience subtle language impairments such as problems with word finding and comprehension, the use of incorrect words, ambiguous referents, loss of verbal fluency, speaking too much or too loudly, repeating ideas, straying from the topic, which worsens in the moderate and severe stages @xcite . this shows that the temporal aspects of spontaneous speech are impacted by this disease. with the advancement of technology, machine learning approaches have been widely applied in the early diagnosis of ad utilizing neuroimaging scans such as magnetic resonance imaging (mri) and positron emission tomography (pet) @xcite . however, this technique for identifying ad patients from control normal (cn) is limited to medical personnel @xcite . @xcite showed that linguistic analysis could be used to identify ad more accurately than other types of cognitive testing. the temporal features of spontaneous speech, such as speech pace, frequency, and length of pauses, are sensitive detectors of the early stage of the illness, allowing an early and straightforward linguistic screening for ad. thus, speech might be a straightforward but crucial characteristic that can be utilized to create potent ai models for ad diagnosis. through groundbreaking advancements in nlp, machines are now able to comprehend human language with unprecedented accuracy, unlocking a new realm of possibilities for data analysis and knowledge extraction @xcite . due to its ability to analyze language patterns and detect small alterations that may signal cognitive deterioration, nlp has grown in prominence in identifying ad @xcite . nlp has been used to diagnose ad largely in high-resource languages like english. however, there is potential for this approach to be adapted to the low-resource languages in developing countries, including those spoken in india. therefore the motivation behind this work is to promote the use of automated nlpbased tools for detecting ad in a low-resource language. such a method will result in an accurate, quick, and economical ad diagnosis. our contri-butions are as follows: ## related works in recent times, there has been significant research in the field of detecting ad using data-driven approaches @xcite . the rapid growth of nlp techniques has led to increased utilization of speech and linguistic features for detecting ad. consequently, machine learning (ml) techniques are extensively employed in this domain. classical ml-based methods require manual feature engineering. such feature extraction methods can vary widely for different languages and can get outdated easily for evolving languages @xcite . driven by the limitation of manual feature engineering of classical ml methods for such a diverse and complex task, in more recent times researchers have leveraged deep learning methods for the detection of ad. @xcite applied three neural models convolutional neural network (cnn), long short-term memory (lstm), and a stronger cnn-lstm for detection of ad against cn on transcripts of dementia bank's dataset of cookie theft picture description. with their initial approach, they were able to achieve an accuracy of 82.8%, 83.7%, and 84.9% for the cnn, lstm, and cnn-lstm architecture respectively. but when fed with pos-tagged data, their best-performing model cnn-lstm model achieved an accuracy of 91.1%. @xcite took a multimodal approach where they also leveraged acoustic features for dementia detection. they used a cnn-attention network and explored audio-based, text-based, and multimodal approaches using both audio and text-based features. they reported that a multimodal approach (c-attention-unified model) using linguistic fea-tures and x-vector (acoustic) features performed best and could detect ad with an accuracy of 77.2% and an f1 score of 0.763. work has also been done for the detection of ad in languages other than english. @xcite proposed an autoencoder-based method to augment the mandarin corpus @xcite with a larger english dataset from dementiabank and used a contrastive learning method based on bert embeddings. with the data augmentation method, they achieved an accuracy of 81.6% in ad prediction. @xcite used a dataset of transcripts of boston cookie theft picture descriptions in the greek language. the samples were obtained from native greek speakers diagnosed with alzheimer's and normal controls. they extracted a total of 10 features based on lexical and syntactic measures and employed naive bayes (nb) and svm with smo (sequential minimal optimization) classifiers. though extensive work has been done in the field of dementia detection for widely used languages such as english and mandarin (chinese) as per who's report, 58% of dementia patients worldwide belong to low-income, middle-income countries @xcite . this highlights the importance of building nlp-based diagnostic tools for lesser-known and low-resource languages. india as one of the lower-middle-income (review, 2023) countries, is estimated to have dementia prevalence in 7.4% of the population, for ages 60 and above, resulting in about 8.8 million indians older than 60 years living with dementia @xcite . hindi is the most spoken language in india and to the best of our knowledge, no work has been done in the hindi language for the detection of alzheimer's disease. thus, we believe our annotated dataset could serve as a stepping stone toward the detection of ad in the hindi language and would contribute to further research in this field. ## dataset dementiabank's pitt corpus is utilized in this study. dementiabank results from an experiment conducted by @xcite that contains audio recordings and transcripts for the boston cookie theft picture description task. the task required the participants to describe a scene, as shown in figure 1 . the transcription for the recordings was done manually using the chat (codes for the human analysis of transcripts) protocol @xcite . the experiment consisted of 292 participants, with 194 having some sort of dementia. this resulted in 309 transcripts for the dementia category, as some participants had multiple recording sessions. this study deals with ad diagnosis. thus, only the 255 transcripts from 168 ad patients and 244 transcripts from 98 cn participants were used in the study. the transcriptions of recordings are accessible in english, which were translated into hindi by three fluent hindi speakers. we decided on manual translation since it is more likely to capture the social subtleties needed for the language. we also created four more datasets using neural machine translation. bleu (bilingual evaluation understudy) score @xcite was used to compare machine translation against manual translation. mbart-50 @xcite , google translate, m2m-100 @xcite , and opus-mt (tiedemann and thottingal, 2020) were used as translation models. the bleu score for each translation model is shown in ## experimental results and discussion we developed benchmarks using a variety of approaches, including traditional machine learning methods, deep learning, and transformer-based models. to evaluate the results for each baseline, we used accuracy, macro-mean-squared-error (mmae), and f1-score (macro) as assessment metrics. accuracy is a trivial evaluation metric in classification tasks. however, we use macro mae and macro f1 to account for imbalanced datasets. using macro mae and macro f1 score gives equal weight to each class, regardless of size. ## conclusion our work presents a novel dataset in the hindi language that classifies the speech of ad patients against cn individuals. ad cannot be cured, so its detection and management become crucial. speech impairment is one of the most common symptoms of ad. hence, we have created this dataset that has the potential to significantly aid in the development of automated, speedy, and cost-effective systems for detecting ad. we also performed benchmarks on the created dataset and achieved the highest accuracy of 0.880 and an f1 score of 0.879 with the xlm-roberta model. such considerable benchmark results encourage further research in the field by extending the dataset and creating more sophisticated and domain-specific models. results show there is room for improvement in constructing superior models.