## 2 Materials and Methods
### Data Analysis
#### 2.3.3 Semi-Automated RFF Estimation  
Semi-automated RFF estimation was performed on all 7721 /ifi/ productions using the MATLAB-based aRFF-AP algorithm, which is described in detail in [PERSON] et al. (2018). The aRFF-AP algorithm estimates RFF using a 9-step process that includes: (1) identifying the voiceless consonant and vowels in each production via high-to-low energy ratios of the acoustic signal, (2) manually confirming and/or modifying the locations of the voiceless consonant and vowels in the acoustic signal, (3) estimating the average \(f_{o}\) and pitch strength of the vowels via the Auditory-SWIPE\({}^{\prime}\) algorithm (S  
The relationship between acoustic and physiologic voicing transitions was assessed by examining acoustic features relative to the termination (t\({}_{\text{off}}\)) or initiation (t\({}_{\text{on}}\)) of voicing. This step is distinct from the development of the aRFF-AP algorithm, as [PERSON] et al. [24] examined acoustic features relative to the intervocalic voicing transitions indicated by manual RFF estimation. To perform this analysis, a literature review was conducted to select a set of acoustic features that showed promise in distinguishing voiced and unvoiced segments, as is the goal of the acoustic features in the semi-automated RFF algorithm (i.e., step 7 of the aRFF-AP algorithm). The acoustic features that best corresponded with the termination or initiation of voicing were then implemented in the aRFF-AP algorithm. Acoustic Feature Selection  
In the aRFF-AP algorithm, acoustic feature trends are examined to identify a state transition in feature values that mark the boundary cycle, or the vocal cycle that distinguishes the vowel from the voiceless consonant. The boundary cycle is offset cycle 10 for voicing offset and onset cycle 1 for voicing onset (see Figure 1). The aRFF-AP algorithm uses three acoustic features to characterize the transition between voiced and unvoiced segments: normalized peak-to-peak amplitude, number of zero crossings, and waveform shape similarity. In addition to the three features included within the aRFF-AP algorithm, a set of 15 new acoustic features were examined with respect to classifying voiced and unvoiced speech segments: (1) autocorrelation, (2) mean cepstral peak prominence, (3) average pitch strength, (4) average voice \(f_{o}\), (5) cross-correlation, (6) low-to-high ratio of spectral energy, (7) median pitch strength, (8) median voice \(f_{o}\), (9) normalized cross-correlation, (10) short-time energy, (11) short-time log energy, (12) short-time magnitude, (13) signal-to-noise ratio, (14) standard deviation of cepstral peak prominence, and (15) standard deviation of voice \(f_{o}\). Of the 18 total features (3 features currently in the aRFF and aRFF-AP algorithms plus 15 new acoustic features), 13 features were calculated directly from the microphone signal. This included autocorrelation, mean and standard deviation of cepstral peak prominence, cross-correlation, low-to-high ratio of spectral energy, normalized cross-correlation, normalized peak-to-peak amplitude, number of zero crossings, short-time energy, short-time log energy, short-time magnitude, signal-to-noise ratio, and waveform shape similarity. The remaining five features were calculated using a processed version of the microphone signal. Specifically, the third step of the aRFF-AP algorithm leverages the Auditory-SWIPE\({}^{\prime}\) algorithm to calculate the \(f_{o}\) contour and pitch strength contour of each /ifi/ production. Three features were calculated from the \(f_{o}\) contour (average, median, and standard deviation of voice \(f_{o}\)), and two features were computed using the pitch strength contour (average and median pitch strength). In addition to examining the 13 acoustic features extracted from the raw microphone signal, filtered versions of these features were also considered.