Automatic Speech Signal Analysis for Clinical Diagnosis and Assessment of Speech Disorders

Baghai-Ravary, Ladan; Beet, S.W.

doi:10.1007/978-1-4614-4574-6

Cited by 25 publications

(13 citation statements)

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“…For diagnosis, management, and treatment of patients, these impaired speech dimensions need to be evaluated through clinical auditory-perceptual assessments. However, such clinical assessments are subjective, time-consuming, and inefficient [2,3].…”

Section: Introductionmentioning

confidence: 99%

Automatic Dysarthric Speech Detection Exploiting Pairwise Distance-Based Convolutional Neural Networks

Janbakhshi

Kodrasi

Bourlard

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Automatic dysarthric speech detection can provide reliable and cost-effective computer-aided tools to assist the clinical diagnosis and management of dysarthria. In this paper we propose a novel automatic dysarthric speech detection approach based on analyses of pairwise distance matrices using convolutional neural networks (CNNs). We represent utterances through articulatory posteriors and consider pairs of phonetically-balanced representations, with one representation from a healthy speaker (i.e., the reference representation) and the other representation from the test speaker (i.e., test representation). Given such pairs of reference and test representations, features are first extracted using a feature extraction front-end, a frame-level distance matrix is computed, and the obtained distance matrix is considered as an image by a CNN-based binary classifier. The feature extraction, distance matrix computation, and CNN-based classifier are jointly optimized in an end-to-end framework. Experimental results on two databases of healthy and dysarthric speakers for different languages and pathologies show that the proposed approach yields a high dysarthric speech detection performance, outperforming other CNN-based baseline approaches.

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Section: Introductionmentioning

confidence: 99%

Automatic Dysarthric Speech Detection Exploiting Pairwise Distance-Based Convolutional Neural Networks

Janbakhshi

Kodrasi

Bourlard

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…It is for such that Computer Aided Diagnosis (CAD) tools are of great interest since they can help diagnosis procedures by using voice recordings as a non-invasive biomarker. They are non-intrusive as they only perform signal processing of voice samples [2].…”

Section: Introductionmentioning

confidence: 99%

Multicondition Training for Noise-Robust Detection of Benign Vocal Fold Lesions From Recorded Speech

2021

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“…Therefore, the evaluation of dysarthria represents a valid clinical support to otolaryngologists, neurologists and speech pathologists for early and differential diagnosis and for documenting disease progression. Generally, this evaluation is performed by using a noninvasive speech signal analysis [8].…”

Section: Introductionmentioning

confidence: 99%

On the Use of Voice Signals for Studying Sclerosis Disease

et al. 2017

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Abstract:Multiple sclerosis (MS) is a chronic demyelinating autoimmune disease affecting the central nervous system. One of its manifestations concerns impaired speech, also known as dysarthria. In many cases, a proper speech evaluation can play an important role in the diagnosis of MS. The identification of abnormal voice patterns can provide valid support for a physician in the diagnosing and monitoring of this neurological disease. In this paper, we present a method for vocal signal analysis in patients affected by MS. The goal is to identify the dysarthria in MS patients to perform an early diagnosis of the disease and to monitor its progress. The proposed method provides the acquisition and analysis of vocal signals, aiming to perform feature extraction and to identify relevant patterns useful to impaired speech associated with MS. This method integrates two well-known methodologies, acoustic analysis and vowel metric methodology, to better define pathological compared to healthy voices. As a result, this method provides patterns that could be useful indicators for physicians in identifying patients affected by MS. Moreover, the proposed procedure could be a valid support in early diagnosis as well as in monitoring treatment success, thus improving a patient's life quality.

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Automatic Speech Signal Analysis for Clinical Diagnosis and Assessment of Speech Disorders

Cited by 25 publications

References 0 publications

Automatic Dysarthric Speech Detection Exploiting Pairwise Distance-Based Convolutional Neural Networks

Automatic Dysarthric Speech Detection Exploiting Pairwise Distance-Based Convolutional Neural Networks

Multicondition Training for Noise-Robust Detection of Benign Vocal Fold Lesions From Recorded Speech

On the Use of Voice Signals for Studying Sclerosis Disease

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