## 3 Results
### RFF Acquisition and Processing  
The speech stimuli used to acquire RFF were evenly distributed across studies. A total of 19 studies (51%) analyzed VCV utterances and 16 studies (43%) analyzed RFF from continuous speech. Only two studies (5%) employed both VCV and continuous speech in their methodologies. The intervocalic consonant /f/ results in the lowest within-subject variation in RFF measures [38] and was unsurprisingly the most common voieless consonant employed in RFF VCV stimuli.  
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]. The algorithm (aRFF) [39] was updated in 2017 and openly shared on Boston University's website at no cost to the user. [PERSON] and colleagues [51] refined the algorithm via incorporating rule-specific criteria for tuning algorithmic parameters based on patient-specific voice characteristics and acoustic recording environment (referred to as aRFF-AP; current version) and, later, by cross-referencing simultaneous high-speed laryngeal imaging to hone in on cycle-specific extraction decisions around the voieless consonant (referred to as aRFF-APH; unreleased) [52].  
These semi-automated algorithms operate in six steps: (1) vowel and voieless fricative detection via high-to-low energy ratios, (2) \(f_{\rm o}\) estimation via autocorrelation (aRFF) or Auditory-SWIPE' (aRFF-AP, aRFF-APH), (3) vocal cycle peak detection of the vowels, (4) vowel-fricative boundary detection via acoustic features, (5) sample rejection for instances that do not meet specified criteria (e.g., less than 10 onset or offset cycles, glottalization, misarticulation, voicing during the voieless consonant), and (6) RFF calculation. Each step of the algorithm is fully automated except for step (1), in which the user must confirm or alter the algorithmically identified location of the fricative within the acoustic signal.  
Of the five studies that focused on algorithmic development, four studies developed automated extraction methods for acoustic signals acquired with a microphone, whereas [PERSON] 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. Unlike the microphone-based algorithms, the accelerometer-based algorithm is fully automated and does not require user input. See Table S1 for more information on the studies devoted to automated RFF extraction.  
A total of 32 studies in this review did not focus on automated RFF development. Of these, 23 studies (72%) used manual estimation to calculate RFF and 9 studies (28%), beginning in 2016, used semi-automated estimation methods. With manual estimation, voicing onset and offset cycles were identified through visual inspection of the acoustic waveform. Praat, a freely available acoustic software [54], was used in 74% (\(n=17\)) of studies that used manual estimation. Praat's \"pulse\" function uses an autocorrelation method to determine cycle\(f_{\rm o}\) from the acoustic waveform. Of the nine studies that used semi-automated estimation, eight employed the aRFF algorithm and only one used aRFF-AP. The aRFF algorithm also uses the autocorrelation method to determine cycle\(f_{\rm o}\) values from the acoustic waveform. Given that manual RFF estimation via Praat and semi-automated RFF estimation via aRFF have been the most widely implemented methods, autocorrelation was the primary technique used to determine\(f_{\rm o}\) in most published studies. See Table S2 for characteristics and data extraction from all 32 studies.