The use of telephone speech recordings for assessment and monitoring of cognitive function in elderly people

Rapčan, Viliam; D’Arcy, Shona; Penard, Nils; Robertson, Ian H.; Reilly, Richard B.

doi:10.21437/interspeech.2009-282

Cited by 9 publications

(1 citation statement)

References 6 publications

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“…In 2009, Rapcan and Reilly developed an algorithm for both detecting and removing breath sounds from speech signals, particularly focusing on its impact on cognitive studies of speech and language [7]. Their approach resulted in a notable increase in discrimination ability, demonstrating the potential of automated breath removal in enhancing classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

Attention-Based Efficient Breath Sound Removal in Studio Audio Recordings

Elgiriyewithana,

Kodikara

2024

NLP &Amp; Information Retrieval

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In this research, we present an innovative, parameter-efficient model that utilizes the attention U-Net architecture for the automatic detection and eradication of non-speech vocal sounds, specifically breath sounds, in vocal recordings. This task is of paramount importance in the field of sound engineering, despite being relatively under-explored. The conventional manual process for detecting and eliminating these sounds requires significant expertise and is extremely time-intensive. Existing automated detection and removal methods often fall short in terms of efficiency and precision. Our proposed model addresses these limitations by offering a streamlined process and superior accuracy, achieved through the application of advanced deep learning techniques. A unique dataset, derived from Device and Produced Speech (DAPS), was employed for this purpose. The training phase of the model emphasizes a log spectrogram and integrates an early stopping mechanism to prevent overfitting. Our model not only conserves precious time for sound engineers but also enhances the quality and consistency of audio production. This constitutes a significant breakthrough, as evidenced by its comparative efficiency, necessitating only 1.9M parameters and a training duration of 3.2 hours - markedly less than the top-performing models in this domain. The model is capable of generating identical outputs as previous models with drastically improved precision, making it an optimal choice.

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Section: Related Workmentioning

confidence: 99%

Attention-Based Efficient Breath Sound Removal in Studio Audio Recordings

Elgiriyewithana,

Kodikara

2024

NLP &Amp; Information Retrieval

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The reality of doing things with (thousands of) words in applied research and clinical settings: A commentary on Clarke et al. (2020)

Holmlund

Diaz-Asper

Elvevåg

2021

Cortex

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Automatic speech analysis for the assessment of patients with predementia and Alzheimer's disease

König

Satt

Sorin

et al. 2015

Alz & Dem Diag Ass & Dis Mo

266

224

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BackgroundTo evaluate the interest of using automatic speech analyses for the assessment of mild cognitive impairment (MCI) and early-stage Alzheimer's disease (AD).MethodsHealthy elderly control (HC) subjects and patients with MCI or AD were recorded while performing several short cognitive vocal tasks. The voice recordings were processed, and the first vocal markers were extracted using speech signal processing techniques. Second, the vocal markers were tested to assess their “power” to distinguish among HC, MCI, and AD. The second step included training automatic classifiers for detecting MCI and AD, using machine learning methods and testing the detection accuracy.ResultsThe classification accuracy of automatic audio analyses were as follows: between HCs and those with MCI, 79% ± 5%; between HCs and those with AD, 87% ± 3%; and between those with MCI and those with AD, 80% ± 5%, demonstrating its assessment utility.ConclusionAutomatic speech analyses could be an additional objective assessment tool for elderly with cognitive decline.

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The use of telephone speech recordings for assessment and monitoring of cognitive function in elderly people

Cited by 9 publications

References 6 publications

Attention-Based Efficient Breath Sound Removal in Studio Audio Recordings

Attention-Based Efficient Breath Sound Removal in Studio Audio Recordings

The reality of doing things with (thousands of) words in applied research and clinical settings: A commentary on Clarke et al. (2020)

Automatic speech analysis for the assessment of patients with predementia and Alzheimer's disease

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