Mobile Digital Recording: Adequacy of the iRig and iOS Device for Acoustic and Perceptual Analysis of Normal Voice

Oliveira, Gisele; Fava, Gaetano; Baglione, Melody; Pimpinella, Michael

doi:10.1016/j.jvoice.2016.05.023

Cited by 23 publications

(12 citation statements)

References 12 publications

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“…In addition, recent studies have shown that smartphone recording is acceptable for acoustic analysis (see e.g. Maryn et al 2017;Oliveira et al 2017;Wu 2017;van der Woerd et al 2020). Thus, recordings were made on the second author's iPhone 6 in a quiet room.…”

Section: Recordingmentioning

confidence: 99%

Mora-timed, stress-timed, and syllable-timed rhythm classes: Clues in English speech production by bilingual speakers

Liu

Takeda

2021

ALing

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A growing body of literature suggests that the world's languages can be classified into three rhythm classes: mora-timed languages, stress-timed languages, and syllable-timed languages. However, scholars cannot agree on which rhythmic measures discriminate rhythm classes most satisfactorily and whether the speech rate factor should be considered. In this study, we analyze speech production by bilingual speakers, and compare their production with that of monolingual speakers and ESL speakers. Our rhythmic metric measure results show that when speech rate is taken into consideration, a combination of the two metric measures for vowels, Varco∆V and vocalic nPVI, is most reliable in discriminating different rhythm classes, while consonants do not seem effective, whether the speech rate factor is included or not.

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Section: Recordingmentioning

confidence: 99%

Mora-timed, stress-timed, and syllable-timed rhythm classes: Clues in English speech production by bilingual speakers

Liu

Takeda

2021

ALing

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“…Para lograr estos objetivos, las investigaciones aquí descritas han usado o desarrollado aplicaciones móviles para smartphone como Vox4Health, VoiceEvalU8 o Voice Analizer, que permiten realizar un análisis acústico de alta fiabilidad con el uso de un smartphone (Cesari et al, 2018;Fujimura et al, 2020;Grillo, 2017;Kojima et al, 2019;Lebacq et al, 2017), lo que favorece una medición más objetiva de la voz en entornos fuera del laboratorio. Sin embargo, los estudios se encuentran limitados a realizar grabaciones con muestras de vocales sostenidas, debido a que las muestras de habla conectada presentan perturbaciones en las grabaciones con smartphone, en comparación a las grabaciones acústicas de laboratorio (Manfredi et al, 2017;Maryn et al, 2017;Oliveira et al, 2017), lo que limita los tipos de análisis acústico que se pueden realizar.…”

Section: Discussionunclassified

Uso del smartphone en telepráctica para trastornos de la voz. Una revisión desde el concepto de Mhealth

Marconi

Morales

Araya

et al. 2022

Rev. investig. logop.

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El uso de los smartphones y el concepto de mobile health (mHealth) es reciente en vocología y sus posibles beneficios en el tratamiento y entrenamiento vocal en contexto de telepráctica. Se realizó una revisión narrativa con el objetivo describir los beneficios de la mHealth a través del smartphone en el contexto de la telepráctica fonoaudiológica de los trastornos vocales. Se buscaron artículos científicos en Pubmed, ScienceDirect y Google Scholar, asociados al uso del smartphone en vocología, considerando voces humanas normales, patológicas y voces sintéticas; relacionados a la intervención, evaluación, valoración, monitoreo, prevención, intervención, supervisión, educación, consulta y entrenamiento vocal. Se revisaron 42 estudios, de los cuales fueron seleccionados 15 de acuerdo a los criterios de inclusión. Los estudios analizados se relacionan con grabación de voz para análisis acústico con smartphone, teleterapia con smartphone y dispositivos periféricos para análisis vocal y seguimiento. Se destaca el potencial de los dispositivos móviles para incrementar accecibilidad, reducir costos y favorecer el seguimiento terapéutico con medidas objetivas en diversos contextos de salud vocal.

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“…반면에 적어도 일부 측정치들은 기존의 기기들을 이용한 측정치들 과 대동소이하다는 연구결과들 (Manfredi et al, 2017;Maryn, Ysenbaert, Zarowski, & Vanspauwen, 2017;Oliveira, Fava, Baglione, & Pimpinella, 2017;Yun, Lee, Lee, & Jin, 2015) (2) CSL로 녹음한 모음 샘플을 듣고, 파형(waveform)에서 진폭 의 변화 추이를 주의 깊게 살펴 시각적으로 확인한다.…”

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“…(3) 스마트폰의 녹음 파일의 파형을 보고 해당 모음연장 발성으 CSL= Computerized Speech Lab; ICC= intraclass correlation coefficient; F0= fundamental frequency; Jitt= jitter; Shim= shimmer; NHR= noise-to-harmonic ratio; CPP= cepstral peak prominence; SR = low/high spectral ratio; CSID = Cepstral Spectral Index of Dysphonia derived from vowel samples. (Yun et al, 2015;Manfredi et al, 2017;Maryn et al, 2017;Oliveira et al, 2017…”

mentioning

confidence: 99%

Clinical Usefulness of Voice Recordings using a Smartphone as a Screening Tool for Voice Disorders

이승진¹,

이광용²,

최홍식³

2018

Commun Sci Disord

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The purpose of the present study was to determine whether selected acoustic measures and estimates differ according to the recording devices, the Computerized Speech Lab (CSL) and a smartphone, and groups. In addition, correlation and reliability between the devices were explored. Lastly, the diagnostic ability of the two devices were compared. Methods: A total of 180 participants (90 patients and 90 controls) participated in the study. Vowel samples of the participants were recorded via the CSL and an android smartphone. For each sample, acoustic parameters including jitter percent (Jitt), shimmer percent (Shim), noise-to-harmonic ratio (NHR), cepstral peak prominence (CPP), L/H ratio, Cepstral Spectral Index of Dysphonia (CSID) were measured. Two-way repeated measures of ANOVA, Pearson correlation analysis, and ROC curve analysis were performed. Results: Interaction effect for Jitt, Shim, NHR, and σCPP between groups and devices were found. F0 measures of the smartphone were higher than those of the CSL. CPP and SR of the CSL and patients were higher than those of the smartphone and controls, respectively. In contrast, CSID and σSR were higher for the smartphone. All the parameters showed positive correlation between devices. There was no difference in the area under curve between the devices, although substantially different cutoff scores were obtained. Conclusion: The current data showed a significant correlation of acoustic measures and no difference in the diagnostic ability between the devices, although differences in several measures and higher cutoff scores of the smartphone were noted. In conclusion, smartphones can be used as a screening tool for voice disorders.

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Mobile Digital Recording: Adequacy of the iRig and iOS Device for Acoustic and Perceptual Analysis of Normal Voice

Cited by 23 publications

References 12 publications

Mora-timed, stress-timed, and syllable-timed rhythm classes: Clues in English speech production by bilingual speakers

Mora-timed, stress-timed, and syllable-timed rhythm classes: Clues in English speech production by bilingual speakers

Uso del smartphone en telepráctica para trastornos de la voz. Una revisión desde el concepto de Mhealth

Clinical Usefulness of Voice Recordings using a Smartphone as a Screening Tool for Voice Disorders

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