Automated Relative Fundamental Frequency Algorithms for Use With Neck-Surface Accelerometer Signals

Groll, Matti D.; Vojtech, Jennifer M.; Hablani, Surbhi; Mehta, Daryush D.; Buckley, Daniel P.; Noordzij, J. Pieter; Stepp, Cara E.

doi:10.1016/j.jvoice.2020.06.001

Cited by 7 publications

(5 citation statements)

References 39 publications

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“…A more recent algorithmic iteration expanded previous work from acoustic microphone signals to signals gathered with neck-surface accelerometers [33]. A neck-surface accelerometer is a small sensor (approximately the size of a dime) placed on the anterior surface of the neck, inferior to the location of the cricoid cartilage but superior to the sternal notch, at midline.…”

Section: Methods Of Rff Computationmentioning

confidence: 99%

“…Five studies focused on algorithmic development for automated RFF extraction [33,39,40,51,52]. Semi-automated algorithmic development began in 2013, in which a custom MATLAB (The MathWorks, Natick, MA, USA) program extracted RFF values from VCV utterances recorded with a microphone [40].…”

Section: Rff Acquisition and Processingmentioning

confidence: 99%

“…Of the five studies that focused on algorithmic development, four studies developed automated extraction methods for acoustic signals acquired with a microphone, whereas Groll et al [33] developed automated methods from neck-surface accelerometer signals. Accelerometers are wearable sensors that have the potential to expand the use of RFF to ambulatory monitoring where it could have utility for long-term clinical tracking.…”

Section: Rff Acquisition and Processingmentioning

confidence: 99%

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A Scoping Literature Review of Relative Fundamental Frequency (RFF) in Individuals with and without Voice Disorders

et al. 2022

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Relative fundamental frequency (RFF) is an acoustic measure that characterizes changes in voice fundamental frequency during voicing transitions. Despite showing promise as an indicator of vocal disorder and laryngeal muscle tension, the clinical adoption of RFF remains challenging, partly due to a lack of research integration. As such, this review sought to provide summative information and highlight next steps for the clinical implementation of RFF. A systematic literature search was completed across 5 databases, yielding 37 articles that met inclusion criteria. Studies most often included adults with and without tension-based voice disorders (e.g., muscle tension dysphonia), though patient and control groups were directly compared in only 32% of studies. Only 11% of studies tracked therapeutic progress, making it difficult to understand how RFF can be used as a clinical outcome. Specifically, there is evidence to support within-person RFF tracking as a clinical outcome, but more research is needed to understand how RFF correlates to auditory-perceptual ratings (strain, effort, and overall severity of dysphonia) both before and after therapeutic interventions. Finally, a marked increase in the use of automated estimation methods was noted since 2016, yet there remains a critical need for a universally available algorithm to support widespread clinical adoption.

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Section: Methods Of Rff Computationmentioning

confidence: 99%

Section: Rff Acquisition and Processingmentioning

confidence: 99%

Section: Rff Acquisition and Processingmentioning

confidence: 99%

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A Scoping Literature Review of Relative Fundamental Frequency (RFF) in Individuals with and without Voice Disorders

et al. 2022

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“…By capturing daily vocal behavior through a neck-surface accelerometer, vocal behaviors associated with excessive or imbalanced laryngeal muscle forces could be identified and monitored via RFF. Although an accelerometer-based RFF algorithm has been developed [61] future work should aim to improve this algorithm by identify physiologically tuned features that can be used to identify the true termination and initiation of vocal fold vibration. Doing so would further improve the clinical relevance of using RFF to assess and track laryngeal muscle tension.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Acoustic Identification of the Voicing Boundary during Intervocalic Offsets and Onsets Based on Vocal Fold Vibratory Measures

et al. 2021

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Methods for automating relative fundamental frequency (RFF)—an acoustic estimate of laryngeal tension—rely on manual identification of voiced/unvoiced boundaries from acoustic signals. This study determined the effect of incorporating features derived from vocal fold vibratory transitions for acoustic boundary detection. Simultaneous microphone and flexible nasendoscope recordings were collected from adults with typical voices (N = 69) and with voices characterized by excessive laryngeal tension (N = 53) producing voiced–unvoiced–voiced utterances. Acoustic features that coincided with vocal fold vibratory transitions were identified and incorporated into an automated RFF algorithm (“aRFF-APH”). Voiced/unvoiced boundary detection accuracy was compared between the aRFF-APH algorithm, a recently published version of the automated RFF algorithm (“aRFF-AP”), and gold-standard, manual RFF estimation. Chi-square tests were performed to characterize differences in boundary cycle identification accuracy among the three RFF estimation methods. Voiced/unvoiced boundary detection accuracy significantly differed by RFF estimation method for voicing offsets and onsets. Of 7721 productions, 76.0% of boundaries were accurately identified via the aRFF-APH algorithm, compared to 70.3% with the aRFF-AP algorithm and 20.4% with manual estimation. Incorporating acoustic features that corresponded with voiced/unvoiced boundaries led to improvements in boundary detection accuracy that surpassed the gold-standard method for calculating RFF.

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“…RFF has been shown to discriminate between the PVH and NPVH conditions in adults [11][12][13], to denote treatment effects after voice therapy [12], and to indicate laryngeal tension [14]. However, its implementation as a clinical measure is hindered by its methodological complexity [15], phonetic constraints [16], and dependence on quasi-periodic signals [8].…”

Section: Introductionmentioning

confidence: 99%

Interaction of Voice Onset Time with Vocal Hyperfunction and Voice Quality

2023

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The main aim of the present work was to investigate whether vocal hyperfunction (VH), perceptual voice quality (VQ), gender, and phonetic environment influence Voice Onset Time (VOT). The investigated group consisted of 30 adults, including 19 women (X = 46.1 ± 13.7 years) and 11 men (X = 47.5 ± 11.0 years), who had either phonotraumatic vocal hyperfunction (PVH) and non-phonotraumatic vocal hyperfunction (NPVH). VQ was judged considering the overall severity of dysphonia (OS) and the subcharacteristics of roughness, breathiness, and strain. Phonetic variables such as vowel stress, syllable stress, and mode of speech task were analyzed. Four samples of syllables with [p] plus vowel or diphthong were retrieved from CAPE-V sentences recordings. Acoustic analysis with Praat comprised VOT, mean fundamental frequency (fo), intensity (SPL dB(A)), and coefficient of variation of fundamental frequency (CV_fo %). VOT was significantly influenced by OS (p ≤ 0.001) but not by vocal VH condition (PVH versus NPVH) (p = 0.90). However, CV_fo was affected by the VH condition (p = 0.02). Gender effects were only found for mean fo (p ≤ 0.001) and SPL (p = 0.01). All VQ sub characteristics (OS, roughness, breathiness, and strain) correlated with VOT (p ≤ 0.001) and SPL (p ≤ 0.001) but not with fo. In summary, VOT was affected by voice quality, while it was not affected by vocal hyperfunction conditions. Therefore, VOT has the potential to objectively describe the onset of voicing in voice diagnostics, and may be one underlying objective characteristic of perceptual vocal quality.

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Automated Relative Fundamental Frequency Algorithms for Use With Neck-Surface Accelerometer Signals

Cited by 7 publications

References 39 publications

A Scoping Literature Review of Relative Fundamental Frequency (RFF) in Individuals with and without Voice Disorders

A Scoping Literature Review of Relative Fundamental Frequency (RFF) in Individuals with and without Voice Disorders

Acoustic Identification of the Voicing Boundary during Intervocalic Offsets and Onsets Based on Vocal Fold Vibratory Measures

Interaction of Voice Onset Time with Vocal Hyperfunction and Voice Quality

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