## 4 Discussion
### Method of RFF Computation
Instead, aligning RFF automated extraction with physiologic events may increase the precision of the measure and provide insight into the underlying physiological factors proposed to influence RFF values. Although the aRFF-APH algorithm demonstrated the highest association with physiologically identified vocal fold vibratory timings, the aRFF-AP algorithm remains the gold-standard method for algorithmically estimating RFF due to its validation across a large dataset (483 independent speakers) and broad range of vocal disorders and severities. A large-scale analysis is still needed with the aRFF-APH algorithm to determine its generalizability across various voice disorders. Although algorithms developed for microphone signals (aRFF, aRFF-AP) are publicly available and relatively easy to use, automated RFF extraction continues to pose issues for clinical practice. First, these RFF algorithms are semi-automated, meaning that they continue to require user interaction to inform extraction methods and determine extraction accuracy. The algorithm interface allows the user to check the accuracy of the voiceless consonant detection from the acoustic waveform and override the automated decision, if necessary, thereby improving extraction accuracy, but also increasing the time needed for processing. Second, all publicly available algorithms [57] require the use of proprietary MATLAB software, including several toolboxes (e.g., econometrics, curvefitting, signal processing) at additional cost. MATLAB is not a common software used by most voice clinics, further limiting the use of algorithms for RFF processing. Ideally, automated RFF methods would expand into freely available software to increase its usability at clinical sites. Third, and perhaps most importantly, automated algorithms can only extract RFF from VCV stimuli. An algorithm that could extract RFF from continuous speech would be helpful for retrospective analyses of pre-established voice databases that include standard reading passages (e.g., Rainbow Passage). Then, researchers could undertake a large-scale analysis across patients of various clinical diagnoses, which would allow for refinement of clinical ranges, cut-off values for classification between vocal health and disorder, and clinical tracking over the course of intervention. 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 time) placed on the anterior surface of the neck, inferior to the location of the cricoid cartilage but superior to the sternal notch, at midline. The sensor captures vibration from the surface of the skin and has the benefit of reduced signal disruption from environmental/background noise and speech friction and/or aspiration noise [58]. Furthermore, the accelerometer does not adequately capture oral resonances, making it difficult-to-impossible to discern what the speaker is saying, thus protecting speaker privacy during communication exchanges. It follows that accelerometers--coupled with smartphone technology--are under development for ambulatory voice monitoring and biofeedback for those at risk for voice disorders (e.g., occupational voice users) and those with current voice disorders [59; 60; 61; 62]. The incorporation of RFF into daily biofeedback could be beneficial for within-subject clinical tracking and/or the identification of maladaptive tension-based vocal patterns in pre-clinical populations. [PERSON] and colleagues [37; 41] have shown that RFF calculated from accelerometer signals are lower when compared to those calculated from simultaneously acquired microphone signals. Therefore, accelerometer-derived RFF values need to be interpreted with caution and only compared to other values obtained from accelerometers.