The Advanced Voice Function Assessment Databases (AVFAD): Tools for Voice Clinicians and Speech Research

Jesus, Luís M. T.; Belo, Inês; Machado, Jessica; Hall, Andreia

doi:10.5772/intechopen.69643

Cited by 9 publications

(6 citation statements)

References 32 publications

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“…The GRBAS and CAPE-V scales are now part of the following comprehensive and unique set of resources developed for clinicians at the University of Aveiro in Portugal: A standardised voice case history form [44,45]; a voice evaluation protocol [18]; a reference voice database [40]. All of these are freely available from the ACSA platform.…”

Section: Discussionmentioning

confidence: 99%

“…Forty-five EP speakers from the Advanced Voice Function Assessment Databases (AVFAD) (see Jesus et al [40] in this book for a detailed description) were used as auditory stimuli. Fifteen participants were selected for anchors, 15 for training and 15 for evaluation.…”

Section: Training Programmementioning

confidence: 99%

“…This resulted in a first prototype of a PowerPoint presentation, shown in Figure 2, based on Japanese audio and video samples [39] that guided the final PowerPoint presentation design that then used audio samples from the Portuguese AVFAD [40].…”

Section: Training Programmementioning

confidence: 99%

See 2 more Smart Citations

Cross-Cultural Adaption of the GRBAS and CAPE-V Scales for Portugal and a New Training Programme for Perceptual Voice Evaluation

Jesus¹,

Tavares²,

Hall³

2017

Advances in Speech-Language Pathology

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Several methods have been proposed for the perceptual evaluation of voice quality, but the GRBAS and Consensus Auditory-Perceptual Evaluation of Voice (CAPE-V) scales are the most widely used and recommended as part of standardised voice evaluation protocols. In this study, cross-cultural adaptation and translation of the GRBAS (the first translation from the original Japanese version) and CAPE-V scales to European Portuguese were carried out following international guidelines. Results from a study of the intra-and inter-rater reliability of the perceptual evaluation of voices with the GRBAS and CAPE-V scales, before and after a training programme, designed according to the most recent American Speech-Language-Hearing Association and Japan Society of Logopedics and Phoniatrics guidelines, are also reported.

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Section: Discussionmentioning

confidence: 99%

Section: Training Programmementioning

confidence: 99%

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Cross-Cultural Adaption of the GRBAS and CAPE-V Scales for Portugal and a New Training Programme for Perceptual Voice Evaluation

Jesus¹,

Tavares²,

Hall³

2017

Advances in Speech-Language Pathology

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“…This is an open-access dataset in the Portuguese language [36] with 363 subjects with no vocal alterations: 250 females and 113 males, and 346 clinically diagnosed subjects with vocal pathology: 249 females and 97 males. The patients are diagnosed with 26 different vocal pathologies, however most of them are not included in SVD.…”

Section: ) Advanced Voice Function Assessment Database (Avfad)mentioning

confidence: 99%

Automatic Voice Disorder Detection Using Self-Supervised Representations

et al. 2023

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Many speech features and models, including Deep Neural Networks (DNN), are used for classification tasks between healthy and pathological speech with the Saarbruecken Voice Database (SVD). However, accuracy values of 80.71% for phrases or 82.8% for vowels /aiu/ are the highest reported for audio samples in SVD when the evaluation includes the wide amount of pathologies in the database, instead of a selection of some pathologies. This paper targets this top performance in the state-of-the-art Automatic Voice Disorder Detection (AVDD) systems. In the framework of a DNN-based AVDD system we study the capability of Self-Supervised (SS) representation learning for describing discriminative cues between healthy and pathological speech. The system processes the SS temporal sequence of features with a single feed-forward layer and Class-Token (CT) Transformer for obtaining the classification between healthy and pathological speech. Furthermore, there is evaluated a suitable data extension of the training set with out-ofdomain data is also evaluated to deal with the low availability of data for using DNN-based models in voice pathology detection. Experimental results using audio samples corresponding to phrases in the SVD dataset, including all pathologies available, show classification accuracy values until 93.36%. This means that the proposed AVDD system achieved accuracy improvements of 4.1% without the training data extension, and 15.62% after the training data extension compared to the baseline system. Beyond the novelty of using SS representations for AVDD, the fact of obtaining accuracies over 90% in these conditions and using the whole set of pathologies in the SVD is a milestone for voice disorder-related research. Furthermore, the study on the amount of in-domain data in the training set related to the system performance show guidance for the data preparation stage. Lessons learned in this work suggest guidelines for taking advantage of DNN, to boost the performance in developing automatic systems for diagnosis, treatment, and monitoring of voice pathologies.

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“…Acoustic analysis is a non-invasive technique that allows the determination of the individual's voice quality and allows a pre-diagnosis, avoiding several invasive tests. It is a technique that can be used to diagnose and study vocal pathologies as it will enable measuring the acoustic signal properties of recorded speech or sustained vowels [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Determination of Harmonic Parameters in Pathological Voices—Efficient Algorithm

et al. 2023

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The harmonic parameters Autocorrelation, Harmonic to Noise Ratio (HNR), and Noise to Harmonic Ratio are related to vocal quality, providing alternative measures of the harmonic energy of a speech signal. They will be used as input resources for an intelligent medical decision support system for the diagnosis of speech pathology. An efficient algorithm is important when implementing it on low-power devices. This article presents an algorithm that determines these parameters by optimizing the window type and length. The method used comparatively analyzes the values of the algorithm, with different combinations of window and size and a reference value. Hamming, Hanning, and Blackman windows with lengths of 3, 6, 12, and 24 glottal cycles and various sampling frequencies were investigated. As a result, we present an efficient algorithm that determines the parameters using the Hanning window with a length of six glottal cycles. The mean difference of Autocorrelation is less than 0.004, and that of HNR is less than 0.42 dB. In conclusion, this algorithm allows extraction of the parameters close to the reference values. In Autocorrelation, there are no significant effects of sampling frequency. However, it should be used cautiously for HNR with lower sampling rates.

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The Advanced Voice Function Assessment Databases (AVFAD): Tools for Voice Clinicians and Speech Research

Cited by 9 publications

References 32 publications

Cross-Cultural Adaption of the GRBAS and CAPE-V Scales for Portugal and a New Training Programme for Perceptual Voice Evaluation

Cross-Cultural Adaption of the GRBAS and CAPE-V Scales for Portugal and a New Training Programme for Perceptual Voice Evaluation

Automatic Voice Disorder Detection Using Self-Supervised Representations

Determination of Harmonic Parameters in Pathological Voices—Efficient Algorithm

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