ArticleJournal of voice : official journal of the Voice Foundation2022
Automated Relative Fundamental Frequency Algorithms for Use With Neck-Surface Accelerometer Signals.
Article in Journal of voice : official journal of the Voice Foundation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Acoustic Metrics of the Strain Dimension of Voice Quality: A Scoping Review.Journal of voice : official journal of the Voice Foundation · 2025Review
- Characterizing Vocal Hyperfunction Using Ecological Momentary Assessment of Relative Fundamental Frequency.Journal of voice : official journal of the Voice Foundation · 2024Article
- Acoustic Identification of the Voicing Boundary during Intervocalic Offsets and Onsets based on Vocal Fold Vibratory Measures.Applied sciences (Basel, Switzerland) · 2021Article
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7 authors.
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Abstract
objectiveRelative fundamental frequency (RFF) has been suggested as a potential acoustic measure of vocal effort. However, current clinical standards for RFF measures require time-consuming manual markings. Previous semi-automated algorithms have been developed to calculate RFF from microphone signals. The current study aimed to develop fully automated algorithms to calculate RFF from neck-surface accelerometer signals for ecological momentary assessment and ambulatory monitoring of voice.
methodsTraining a set of 2646 /vowel-fricative-vowel/ utterances from 317 unique speakers, with and without voice disorders, was used to develop automated algorithms to calculate RFF values from neck-surface accelerometer signals. The algorithms first rejected utterances with poor vowel-to-noise ratios, then identified fricative locations, then used signal features to determine voicing boundary cycles, and finally calculated corresponding RFF values. These automated RFF values were compared to the clinical gold-standard of manual RFF calculated from simultaneously collected microphone signals in a novel test set of 639 utterances from 77 unique speakers.
resultsAutomated accelerometer-based RFF values resulted in an average mean bias error (MBE) across all cycles of 0.027 ST, with an MBE of 0.152 ST and -0.252 ST in the offset and onset cycles closest to the fricative, respectively.
conclusionAll MBE values were smaller than the expected changes in RFF values following successful voice therapy, suggesting that the current algorithms could be used for ecological momentary assessment and ambulatory monitoring via neck-surface accelerometer signals.
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