Acoustic Analysis and Non Linear Dynamics Applied to Voice Pathology Detection: A Review

Orozco-Arroyave, Juan Rafael; Vargas-Bonilla, Jesús Francisco; Delgado-Trejos, Edilson

doi:10.2174/2210686311202020096

Cited by 14 publications

(3 citation statements)

References 58 publications

(58 reference statements)

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“…Some research works demonstrated that pitch, formants and NHR, HHR, GNR etc. can represent some characteristics of the pathological voice such as hoarseness, breathiness and roughness [12,13], which can all be seen on spectrograms. In STFT, each file use 10 ms hamming window segments, with 50% overlap between consecutive windows.…”

Section: B Pre-processing For Input Data To Cnnmentioning

confidence: 99%

Convolutional Neural Networks for Pathological Voice Detection

Soraghan

Lowit

et al. 2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Acoustic analysis using signal processing tools can be used to extract voice features to distinguish whether a voice is pathological or healthy. The proposed work uses spectrogram of voice recordings from a voice database as the input to a Convolutional Neural Network (CNN) for automatic feature extraction and classification of disordered and normal voice. The novel classifier achieved 88.5%, 66.2% and 77.0% accuracy on training, validation and testing data set respectively on 482 normal and 482 organic dysphonia speech files. It reveals that the proposed novel algorithm on the Saarbruecken Voice Database can effectively been used for screening pathological voice recordings.

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Section: B Pre-processing For Input Data To Cnnmentioning

confidence: 99%

Convolutional Neural Networks for Pathological Voice Detection

Soraghan

Lowit

et al. 2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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“…One of them is by looking for new meaningful features, using a well known classifier. In that regard multiple research lines have been proposed, from pitch related features [5], cepstral analysis [6]- [8], non-linear analysis [9], [10] or wavelet transformation [11]. Other common route is researching a good new classifier which improves the already known ones, since new machine learning techniques are being constantly researched, and many of them have been applied to this particular field using already known features [12].…”

Section: Introductionmentioning

confidence: 99%

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

2021

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“…This family of methods aims to measure the atypical acoustic resonance resulting from VPD as an objective proxy for hypernasal speech. Previous work in this area can be categorized broadly in two groups: engineered features based on statistical signal processing [20] and supervised methods based on machine learning [21]. The simple acoustic features fail to capture the complex manifestation of hypernasality in speech, as there is a great deal of person-to-person variability [22].…”

Section: Introductionmentioning

confidence: 99%

Robust Estimation of Hypernasality in Dysarthria with Acoustic Model Likelihood Features

Saxon,

Tripathi,

Jiao

et al. 2019

Preprint

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Hypernasality is a common characteristic symptom across many motor-speech disorders. For voiced sounds, hypernasality introduces an additional resonance in the lower frequencies and, for unvoiced sounds, there is reduced articulatory precision due to air escaping through the nasal cavity. However, the acoustic manifestation of these symptoms is highly variable, making hypernasality estimation very challenging, both for human specialists and automated systems. Previous work in this area relies on either engineered features based on statistical signal processing or machine learning models trained on clinical ratings. Engineered features often fail to capture the complex acoustic patterns associated with hypernasality, whereas metrics based on machine learning are prone to overfitting to the small disease-specific speech datasets on which they are trained.Here we propose a new set of acoustic features that capture these complementary dimensions. The features are based on two acoustic models trained on a large corpus of healthy speech. The first acoustic model aims to measure nasal resonance from voiced sounds, whereas the second acoustic model aims to measure articulatory imprecision from unvoiced sounds. To demonstrate that the features derived from these acoustic models are specific to hypernasal speech, we evaluate them across different dysarthria corpora. Our results show that the features generalize even when training on hypernasal speech from one disease and evaluating on hypernasal speech from another disease (e.g. training on Parkinson's disease, evaluation on Huntington's disease), and when training on neurologically disordered speech but evaluating on cleft palate speech.

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Acoustic Analysis and Non Linear Dynamics Applied to Voice Pathology Detection: A Review

Cited by 14 publications

References 58 publications

Convolutional Neural Networks for Pathological Voice Detection

Convolutional Neural Networks for Pathological Voice Detection

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

Robust Estimation of Hypernasality in Dysarthria with Acoustic Model Likelihood Features

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