Accuracy of Acoustic Analysis Measurements in the Evaluation of Patients With Different Laryngeal Diagnoses

Lopes, Leonardo Wanderley; Simões, Layssa Batista; Silva, Jocélio Delfino da; Evangelista, Deyverson da Silva; Ugulino, Ana Celiane da Nóbrega e; Silva, Priscila Oliveira Costa; Vieira, Vinícius Jefferson Dias

doi:10.1016/j.jvoice.2016.08.015

Cited by 48 publications

(39 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Despite the unquestionable improvement in patients' vocal quality, no statistically significant results were obtained in the acoustic evaluation before and after treatment, probably due to the small sample size and also because each acoustic parameter was analysed separately [33,34]. Furthermore, these results are in agreement with the literature, which considers that perceptive evaluation is actually the most valid and reliable measure to evaluate voice quality [35].…”

Section: Discussionsupporting

confidence: 74%

Evaluation of Postural Changes Using Dynamic Posturography after Speech Rehabilitation in Patients with Voice Disorders: A Longitudinal Study

Caçador

Papoila

Brás-Geraldes

et al. 2019

Folia Phoniatr Logop

View full text Add to dashboard Cite

Background: The association between voice and body posture is consensual across the scientific literature and seems to be established both ways. Any changes in normal posture can influence the mechanisms of vocal production; on the other hand, vocal rehabilitation can influence posture. Objectives: This study aimed to evaluate the postural pattern in subjects with organic voice disorders before and after speech rehabilitation, using computerised dynamic posturography (CDP). Methods: In this prospective cohort study, 21 patients affected by dysphonia caused by benign vocal fold lesions, never treated with speech therapy/vocal training, were submitted to a posturographic analysis using CDP before and after vocal rehabilitation/therapy. Each patient underwent an accurate voice and ear, nose, and throat (ENT) anamnesis, a general ENT examination, a rigid and flexible laryngoscopy, a videolaryngostroboscopy, an acoustic voice analysis including aerodynamic evaluation, and a perceptual evaluation of voice using the Grade, Roughness, Breathiness, Asthenia, and Strain (GRBAS) scale and the Voice Handicap Index (VHI) questionnaire, before and after vocal therapy. Fifteen healthy age- and sex-matched volunteers were also submitted to a posturographic analysis on the day of recruitment and 4 weeks later. Results: All patients showed an improvement in voice quality after vocal training. The VHI decreased in all subjects, and the GRBAS scale showed a decrease in all parameters in each vowel (/a/, /i/, /e/) and in spontaneous speech (p < 0.001 for all). Posturographic results showed an improvement in equilibrium score, in conditions 2–6 and composite score. Strategic analysis results showed an improvement in conditions 1–6. Conclusions: The posturographic analysis showed a significant difference in the visual, vestibular, and proprioceptive component of posture after voice therapy. These results showed that dysphonic patients changed their postural patterns after an effective voice treatment, with an improvement in postural performance. It seems like modifications of breathing pattern and voice production techniques led to objective and measurable postural changes.

show abstract

Section: Discussionsupporting

confidence: 74%

Evaluation of Postural Changes Using Dynamic Posturography after Speech Rehabilitation in Patients with Voice Disorders: A Longitudinal Study

Caçador

Papoila

Brás-Geraldes

et al. 2019

Folia Phoniatr Logop

View full text Add to dashboard Cite

show abstract

“…Analysis of the combined acoustic measures in the discrimination of the predominant voice quality again revealed that the GNE measure appeared in all acceptable combinations found. The F 0 measure was present in most of the combinations when discriminating predominant voice quality, which attests to the results found in previous studies (8,13,18,29,30) in which the fundamental frequency appeared to be an interesting measure when discriminating voice quality. This is probably because it is related, in physiological terms, to the neuromuscular condition and vocal fold mucosa vibration regularity, and in acoustic and perceptual terms, it is directly related to the sound signal periodicity (6,9,11,30) .…”

Section: Traditional Acoustic and Formantic Measures In The Discriminsupporting

confidence: 89%

“…The general grade and roughness seem to be parameters more related to F 0 (28,30) . The mean F 0 values are higher both in general grade and in vocal tension, and the F 0 standard deviation values are also high in rough voices.…”

Section: Traditional Acoustic and Formantic Measures In The Discriminmentioning

confidence: 90%

“…The combined use of measures for the evaluation, characterization and classification of the voice signal may therefore better represent voice production characteristics and highlight manifestations that would not be detected with the use of isolated measures. Other studies (3,28,30) have shown that a combination of perturbation and noise measures improves the discrimination between signals with and without voice deviations. However, in terms of this study, it may be concluded that combining measures related to vocal tract adjustments with traditional perturbation and noise measures can improve the classification of the voice deviation intensity and type and provide insights into the source-filter interaction in patients with voice deviations.…”

Section: Traditional Acoustic and Formantic Measures In The Discriminmentioning

confidence: 97%

See 1 more Smart Citation

Acurácia das medidas acústicas tradicionais e formânticas na avaliação da qualidade vocal

Lopes

Alves

Evangelista

et al. 2018

CoDAS

Self Cite

View full text Add to dashboard Cite

RESUMO Objetivo Investigar a acurácia das medidas acústicas, isoladas e combinadas, na discriminação da intensidade do desvio vocal (GG) e da qualidade vocal predominante (QVP) em pacientes disfônicos. Método Participaram 302 pacientes do gênero feminino, com queixa vocal. A partir da vogal /ɛ/ sustentada, foram extraídas as medidas acústicas de média e desvio padrão (DP) da frequência fundamental (F0), o jitter, o shimmer e o Glottal to noise excitation (GNE) e a média dos três primeiros formantes (F1, F2, F3). A avaliação perceptivo-auditiva do GG e QVP foi realizada por três fonoaudiólogos especialistas em voz. Resultados Isoladamente, apenas o GNE obteve desempenho satisfatório na discriminação do GG e da QVP. Houve uma melhora na classificação do GG e QVP com a combinação das medidas acústicas. A média de F0, F2 e GNE (saudável × desvio leve a moderado), DP de F0, F1 e F3 (leve a moderado × desvio moderado), Jitter e GNE (moderado × desvio intenso) foram as melhores combinações para discriminar o GG. As melhores combinações para discriminação da QVP foram média de F0, Shimmer e GNE (saudável × rugosa), F3 e GNE (saudável × soprosa), média de F0, F3 e GNE (rugosa × tensa), média de F0, F1 e GNE (soprosa × tensa). Conclusão De forma isolada, o GNE mostrou-se o único parâmetro acústico capaz de discriminar o GG e a QVP. Houve um ganho no desempenho da classificação com a combinação das medidas acústicas tradicionais e formânticas, tanto para a discriminação do GG como da QVP.

show abstract

“…Shimmer value is associated to voice quality [2][3][4][5][6][7], state of mind [8][9][10][11][12][13], age [14] and gender [15] of people. There are many research works that use shimmer (among other measures) with goals ranging from pathologies detection [6,16,17] to the improvement of human-machine interfaces through the estimation of the intensionality of a spoken phrase [19]. Regarding synthesized voices, Yamasaki et al show in [18] that a certain shimmer level increases the degree of naturalness.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Shimmer Approximation in Synthesized Audio Signal

García

Destéfanis

2018

Communications in Computer and Information Science

View full text Add to dashboard Cite

Shimmer is a classical acoustic measure of the amplitude perturbation of a signal. This kind of variation in the human voice allow to characterize some properties, not only of the voice itself, but of the person who speaks. During the last years deep learning techniques have become the state of the art for recognition tasks on the voice. In this work the relationship between shimmer and deep neural networks is analyzed. A deep learning model is created. It is able to approximate shimmer value of a simple synthesized audio signal (stationary and without formants) taking the spectrogram as input feature. It is concluded firstly, that for this kind of synthesized signal, a neural network like the one we proposed can approximate shimmer, and secondly, that the convolution layers can be designed in order to preserve the information of shimmer and transmit it to the following layers.

show abstract

Accuracy of Acoustic Analysis Measurements in the Evaluation of Patients With Different Laryngeal Diagnoses

Cited by 48 publications

References 66 publications

Evaluation of Postural Changes Using Dynamic Posturography after Speech Rehabilitation in Patients with Voice Disorders: A Longitudinal Study

Evaluation of Postural Changes Using Dynamic Posturography after Speech Rehabilitation in Patients with Voice Disorders: A Longitudinal Study

Acurácia das medidas acústicas tradicionais e formânticas na avaliação da qualidade vocal

Deep Neural Networks for Shimmer Approximation in Synthesized Audio Signal

Contact Info

Product

Resources

About