Ki(ng) in the North : effects of duration, boundary and pause on post-nasal [ɡ]-presence
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![Figure 2: Example spectrograms and waveforms of song with [ɡ]-absence (left) and thing with [ɡ]-presence (right).](https://thumb-us.123doks.com/thumbv2/123dok_us/1775539.131581/12.595.87.521.567.726/figure-example-spectrograms-waveforms-absence-thing-presence-right.webp)
![Figure 3: Change in apparent time of pre-pausal [ɡ]-presence, based on a sample of 32 speakers](https://thumb-us.123doks.com/thumbv2/123dok_us/1775539.131581/13.595.138.451.508.696/figure-change-apparent-pausal-presence-based-sample-speakers.webp)
![Figure 4: Rate of [ɡ]-presence by boundary strength and following segment.](https://thumb-us.123doks.com/thumbv2/123dok_us/1775539.131581/15.595.107.485.71.265/figure-rate-presence-boundary-strength-following-segment.webp)
![Figure 5: The relationship between sonorant duration, following pause duration, and [ɡ]-presence for pre-consonantal (ng) tokens](https://thumb-us.123doks.com/thumbv2/123dok_us/1775539.131581/16.595.101.507.72.284/figure-relationship-sonorant-duration-following-duration-presence-consonantal.webp)
![Table 4: Best-itting logistic regression model; [ɡ]-presence as application value, with random intercepts of speaker and word.](https://thumb-us.123doks.com/thumbv2/123dok_us/1775539.131581/18.595.166.439.299.391/table-itting-logistic-regression-presence-application-intercepts-speaker.webp)
![Figure 7: Visualization of the distribution of pauses and IP boundaries in this study’s dataset, and the effect they have upon [ɡ]-presence.](https://thumb-us.123doks.com/thumbv2/123dok_us/1775539.131581/19.595.158.431.69.270/figure-visualization-distribution-pauses-boundaries-dataset-effect-presence.webp)
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