Dementia Assessment Using Mandarin Speech with an Attention-based Speech Recognition Encoder

• URL: http://arxiv.org/abs/2310.03985v2
• Date: Fri, 15 Dec 2023 13:07:44 GMT
• Title: Dementia Assessment Using Mandarin Speech with an Attention-based Speech Recognition Encoder
• Authors: Zih-Jyun Lin, Yi-Ju Chen, Po-Chih Kuo, Likai Huang, Chaur-Jong Hu, Cheng-Yu Chen
• Abstract summary: This paper utilizes a speech recognition model to construct a dementia assessment system tailored for Mandarin speakers. We collected Mandarin speech data from 99 subjects and acquired their clinical assessments from a local hospital. We achieved an accuracy of 92.04% in Alzheimer's disease detection and a mean absolute error of 9% in clinical dementia rating score prediction.
• Score: 0.4369058206183195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dementia diagnosis requires a series of different testing methods, which is complex and time-consuming. Early detection of dementia is crucial as it can prevent further deterioration of the condition. This paper utilizes a speech recognition model to construct a dementia assessment system tailored for Mandarin speakers during the picture description task. By training an attention-based speech recognition model on voice data closely resembling real-world scenarios, we have significantly enhanced the model's recognition capabilities. Subsequently, we extracted the encoder from the speech recognition model and added a linear layer for dementia assessment. We collected Mandarin speech data from 99 subjects and acquired their clinical assessments from a local hospital. We achieved an accuracy of 92.04% in Alzheimer's disease detection and a mean absolute error of 9% in clinical dementia rating score prediction.

Related papers

• Self-supervised Speech Models for Word-Level Stuttered Speech Detection [66.46810024006712]
  We introduce a word-level stuttering speech detection model leveraging self-supervised speech models. Our evaluation demonstrates that our model surpasses previous approaches in word-level stuttering speech detection.
  arXiv  Detail & Related papers  (2024-09-16T20:18:20Z)
• Prosody-Driven Privacy-Preserving Dementia Detection [3.9530780161144667]
  We aim to anonymize embeddings while preserving the diagnostic utility for dementia detection. We propose a novel approach that leverages domain knowledge to disentangle prosody features relevant to dementia from speaker embeddings.
  arXiv  Detail & Related papers  (2024-07-03T19:34:47Z)
• Identification of Cognitive Decline from Spoken Language through Feature Selection and the Bag of Acoustic Words Model [0.0]
  The early identification of symptoms of memory disorders plays a significant role in ensuring the well-being of populations. The lack of standardized speech tests in clinical settings has led to a growing emphasis on developing automatic machine learning techniques for analyzing naturally spoken language. The work presents an approach related to feature selection, allowing for the automatic selection of the essential features required for diagnosis from the Geneva minimalistic acoustic parameter set and relative speech pauses.
  arXiv  Detail & Related papers  (2024-02-02T17:06:03Z)
• Automatically measuring speech fluency in people with aphasia: first achievements using read-speech data [55.84746218227712]
  This study aims at assessing the relevance of a signalprocessingalgorithm, initially developed in the field of language acquisition, for the automatic measurement of speech fluency.
  arXiv  Detail & Related papers  (2023-08-09T07:51:40Z)
• Multilingual Alzheimer's Dementia Recognition through Spontaneous Speech: a Signal Processing Grand Challenge [18.684024762601215]
  This Signal Processing Grand Challenge (SPGC) targets a difficult automatic prediction problem of societal and medical relevance. The Challenge has been designed to assess the extent to which predictive models built based on speech in one language (English) generalise to another language (Greek)
  arXiv  Detail & Related papers  (2023-01-13T14:09:13Z)
• Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection [62.23830810096617]
  Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care to delay further progression. This paper presents the development of a state-of-the-art Conformer based speech recognition system built on the DementiaBank Pitt corpus for automatic AD detection.
  arXiv  Detail & Related papers  (2022-06-23T12:50:55Z)
• Investigation of Data Augmentation Techniques for Disordered Speech Recognition [69.50670302435174]
  This paper investigates a set of data augmentation techniques for disordered speech recognition. Both normal and disordered speech were exploited in the augmentation process. The final speaker adapted system constructed using the UASpeech corpus and the best augmentation approach based on speed perturbation produced up to 2.92% absolute word error rate (WER)
  arXiv  Detail & Related papers  (2022-01-14T17:09:22Z)
• Multi-modal fusion with gating using audio, lexical and disfluency features for Alzheimer's Dementia recognition from spontaneous speech [11.34426502082293]
  This paper is a submission to the Alzheimer's Dementia Recognition through Spontaneous Speech (ADReSS) challenge. It aims to develop methods that can assist in the automated prediction of severity of Alzheimer's Disease from speech data.
  arXiv  Detail & Related papers  (2021-06-17T17:20:57Z)
• NUVA: A Naming Utterance Verifier for Aphasia Treatment [49.114436579008476]
  Assessment of speech performance using picture naming tasks is a key method for both diagnosis and monitoring of responses to treatment interventions by people with aphasia (PWA) Here we present NUVA, an utterance verification system incorporating a deep learning element that classifies 'correct' versus'incorrect' naming attempts from aphasic stroke patients. When tested on eight native British-English speaking PWA the system's performance accuracy ranged between 83.6% to 93.6%, with a 10-fold cross-validation mean of 89.5%.
  arXiv  Detail & Related papers  (2021-02-10T13:00:29Z)
• Bulbar ALS Detection Based on Analysis of Voice Perturbation and Vibrato [68.97335984455059]
  The purpose of this work was to verify the sutability of the sustain vowel phonation test for automatic detection of patients with ALS. We proposed enhanced procedure for separation of voice signal into fundamental periods that requires for calculation of measurements.
  arXiv  Detail & Related papers  (2020-03-24T12:49:25Z)
• Identification of Dementia Using Audio Biomarkers [15.740689461116762]
  The objective of this work is to use speech processing and machine learning techniques to automatically identify the stage of dementia. Non-linguistic acoustic parameters are used for this purpose, making this a language independent approach. We analyze the contribution of various types of acoustic features such as spectral, temporal, cepstral their feature-level fusion and selection towards the identification of dementia stage.
  arXiv  Detail & Related papers  (2020-02-27T13:54:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.