9.2 Read dataset
The dataset comes from one of my projects; this is a subset from one speaker. It provides acoustic correlates that can be used to distinguish between guttural and non-guttural consonants. The former group comprises: uvular, pharyngealised and pharyngeal consonants; the latter, plain, velar and glottal. These six classes were produced in three different vowel environments /i: a: u:/. In each class, there were multiple consonants (total 21).
Acoustic measurements are used to quantify supralaryngeal (formant distances on the Bark scale) and laryngeal (voice quality) measurements.
## Rows: 402 Columns: 25
## ── Column specification ───────────────────────────────────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): contextN, context
## dbl (23): CPP, Energy, H1A1c, H1A2c, H1A3c, H1H2c, H2H4c, H2KH5Kc, H42Kc, HN...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.## # A tibble: 402 × 25
## contextN context CPP Energy H1A1c H1A2c H1A3c H1H2c H2H4c H2KH5Kc
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 G_01 Guttural 23.5 22.8 15.2 16.0 -3.30 -1.18 2.65 23.2
## 2 G_02 Guttural 25.7 22.8 13.2 11.6 -5.92 -0.739 1.65 16.5
## 3 G_03 Guttural 19.6 9.16 16.5 18.5 0.604 -0.928 4.50 18.5
## 4 G_04 Guttural 23.6 17.2 16.0 20.1 -7.63 0.782 2.42 28.2
## 5 G_05 Guttural 26.0 15.6 14.5 14.6 -6.73 0.0442 3.13 27.6
## 6 G_06 Guttural 24.5 14.4 16.6 16.6 -5.46 1.34 4.51 21.6
## 7 G_07 Guttural 23.1 18.6 15.0 15.8 -5.94 -0.211 2.11 26.3
## 8 G_08 Guttural 22.2 9.04 17.1 18.4 2.10 -0.742 3.41 19.1
## 9 G_09 Guttural 28.4 25.5 12.5 15.1 -3.33 -1.59 3.80 25.6
## 10 G_10 Guttural 28.2 15.8 14.7 16.3 -0.0528 0.108 3.50 22.5
## # ℹ 392 more rows
## # ℹ 15 more variables: H42Kc <dbl>, HNR05 <dbl>, HNR15 <dbl>, HNR25 <dbl>,
## # HNR35 <dbl>, SHR <dbl>, soe <dbl>, Z1mnZ0 <dbl>, Z2mnZ1 <dbl>,
## # Z3mnZ2 <dbl>, Z4mnZ3 <dbl>, F0Bark <dbl>, A1mnA2 <dbl>, A1mnA3 <dbl>,
## # A2mnA3 <dbl>dfPharV2 <- dfPharV2 %>%
mutate(context = factor(context, levels = c("Non-Guttural", "Guttural")))
dfPharV2## # A tibble: 402 × 25
## contextN context CPP Energy H1A1c H1A2c H1A3c H1H2c H2H4c H2KH5Kc
## <chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 G_01 Guttural 23.5 22.8 15.2 16.0 -3.30 -1.18 2.65 23.2
## 2 G_02 Guttural 25.7 22.8 13.2 11.6 -5.92 -0.739 1.65 16.5
## 3 G_03 Guttural 19.6 9.16 16.5 18.5 0.604 -0.928 4.50 18.5
## 4 G_04 Guttural 23.6 17.2 16.0 20.1 -7.63 0.782 2.42 28.2
## 5 G_05 Guttural 26.0 15.6 14.5 14.6 -6.73 0.0442 3.13 27.6
## 6 G_06 Guttural 24.5 14.4 16.6 16.6 -5.46 1.34 4.51 21.6
## 7 G_07 Guttural 23.1 18.6 15.0 15.8 -5.94 -0.211 2.11 26.3
## 8 G_08 Guttural 22.2 9.04 17.1 18.4 2.10 -0.742 3.41 19.1
## 9 G_09 Guttural 28.4 25.5 12.5 15.1 -3.33 -1.59 3.80 25.6
## 10 G_10 Guttural 28.2 15.8 14.7 16.3 -0.0528 0.108 3.50 22.5
## # ℹ 392 more rows
## # ℹ 15 more variables: H42Kc <dbl>, HNR05 <dbl>, HNR15 <dbl>, HNR25 <dbl>,
## # HNR35 <dbl>, SHR <dbl>, soe <dbl>, Z1mnZ0 <dbl>, Z2mnZ1 <dbl>,
## # Z3mnZ2 <dbl>, Z4mnZ3 <dbl>, F0Bark <dbl>, A1mnA2 <dbl>, A1mnA3 <dbl>,
## # A2mnA3 <dbl>