Creating Data Set

As with the pilot study, the recordings were divided into the reading section, retelling section, and interview. A fourth sound file was created from which laughter, excessive creaky voice, or falsetto had been removed. Upon reflecting on the successes and difficulties of the pilot study, it was decided that it would be best to use all recordings in full, regardless of length, so as to maximise the data. Instead of working from the file’s temporal midpoint to collect F0 and /s/ observations, as

had been done in the pilot study, tokens were collected from the entire duration of the recordings for the main study.

The sound files for reading, retelling, and the edited interview files for each participant were also run through the Lennes (2016) Praat script to collect the F0 data for the participants. While it was stated above that Lennes et al. (2016) found that one only needed to consider a brief amount of voiced speech in order to accurately determine mean F0 measures, it was decided that it would be beneficial to obtain F0 measurements for the entire duration of the interview. This method would give a full sampling for every participant, which removed the concern about having to deduce the best point in the sample at which to begin collecting data. The F0 data was edited to remove outliers, non-modal speech, and to account for pitch doubling and halving, as discussed above. F0 values below 90Hz and above 400Hz were excluded. As discussed above, speakers categorised as female in previous work have typically had an F0 mean around 200Hz. Since all the participants in this study identify themselves as female, 90Hz was deemed an appropriate cut off point for the bottom of their modal range and 400Hz was an acceptable cut off point for the top of their modal range. While these ranges may not account for all speakers that identify as female, initial scans of the data indicated this range would be appropriate for the present group of participants.

The minimum of 90Hz was increased from the 70Hz threshold used in the pilot study to further account for creaky voice, as this feature was prominent in this group of speakers. Data were removed that were two or more standard deviations above or below the mean, on an individual speaker basis. The change to remove outliers according to standard deviation instead of quantiles was also included to account for creaky voice in the recordings. Due to creaky voice being frequently present in the data, basing the bottom of the threshold on the 5% quantile did not consistently eliminate evidence of creak. However, basing the threshold on individual standard deviations did eliminate creak data.

Each sound file was transcribed using the software ELAN (2018) in order to time- align the signal with its corresponding transcription. After the transcription was

complete, the files were forced-aligned using the software package FAVE

(Rosenfelder et al., 2014). This produced a Praat TextGrid aligning the transcribed phonemes with the relevant sections of the audio file.

All files were then manually checked in order to ensure the /s/ tokens were correctly identified and the boundaries precise. Observations of /s/ were collected for the entire duration of the recording, as with the F0 observations. As before, all /s/ tokens that were adjacent to another sibilant were removed (Figure 3). Tokens were also removed if there was interference from ambient noise or overlapping speech. Finally, /s/ tokens in which voicing was present were removed. The remaining tokens were run through a Praat script, which would log each occurrence of /s/ for centre of gravity, standard deviation, skewness, kurtosis, duration, amplitude, and peak. This script adapted from Fecher (2011) can be seen in Appendix 5.

Each /s/ token was coded for syllable position (onset, coda, and ambisyllabic), phrasal position (phrase-initial, -medial, -final), and prosodic prominence (stressed vs. unstressed). Having consulted previous work, including Podesva and Van Hofwegen (2016) and Stuart-Smith et al. (2003), syllable position was prioritised in this study, versus the position in the word as per the pilot study design. For a similar reason, phrase position was also labelled.

All /s/ tokens were coded for the syllable in which they appeared. However, it was not possible to determine whether some /s/ tokens were part of the coda of one syllable or in the onset of the next. /s/ tokens in cases of this type were labelled ambisyllabic. Predominantly, these ambisyllabic tokens appeared in intervocalic consonant clusters in words such as “extra”.

Coding for placement in the phrase was based on Stuart-Smith, Sonderegger, Rathcke, and Macdonald’s (2015) study of stop consonants. A phrase was defined as “the interval between two intervals of silence of at least 150 ms” (Stuart-Smith et al., 2015, p. 515). If the /s/ token was in the first word of a phrase it was labelled as phrase-initial, and if it was in the last word of a phrase it was labelled as phrase- final. All other tokens were labelled as phrase-medial. An example of syllable and phrase coding can be seen in Figure 4 below.

In Figure 4, there is speech and then a pause, labelled as “sp”, which exceeds 150ms. In this case, the /s/ token in the word “step” is classified as phrase-medial, but the two /s/ tokens in the word “sister” are both classed as phrase-final. The /s/ token in “step” and the first /s/ token in “sister” are both classed as syllable-onset, while the second /s/ token in the word “sister” is coded as syllable-coda.

Finally, syllable stress was considered. In previous research, such as Holmes-Elliott & Levon (Holmes-Elliott and Levon, 2017) or Podesva and Van Hofwegen (2016), /s/ tokens have been described as stressed or unstressed. However, in practice making this distinction was not straightforward. As Cruttenden (1986)

acknowledges when discussing stress, “[a]ny description of English word-stress rules inevitably involves a large number of exceptions” (p. 19). In order to match previous research like Holmes-Elliott & Levon (2017) and Podesva and Van

Hofwegen (2016), and to simplify stress classification, syllable stress was treated as a simple binary distinction prominent versus not prominent. Therefore, even when an /s/ token appeared in a word with multiple stresses, all levels of stress were simply counted as stressed without separating the levels of stress. If the syllable was not judged to be stress-bearing at all, it was marked as unstressed, or not prominent.

In the example in Figure 4 above, both /s/ tokens in the word “sister” are in a prominent syllable, and therefore both are coded as stressed.

In order to ensure best practice when classifying the stress patterns, a subset of the material was independently coded for syllable stress by one of the project’s

supervisors, using the same criteria. Olivia’s interview was chosen, as it is one of the longer interviews and makes up roughly 5% of the total corpus. An agreement rate threshold of 80% was deemed satisfactory for present purposes, bearing in mind that judgements of relative prosodic prominence are inevitably subjective (Wells, 2006, p. 248).

The final corpus, at 18 hours 10 minutes in length, yielded 12,368 /s/ tokens and 668,786 F0 observations across all 22 participants.