Statistic functions¶

Transcription¶

ests.phon_stats.transcribe()

Transcribes a word into its sounds. The word is split into syllables (syllabify) and every syllable is read by the rules of the Spanish orthography; the characters that are no Spanish letters (digits, hyphens) are left out.

Spelling	Sound	Example
`a`, `á`; `e`, `é`; `i`, `í`; `o`, `ó`; `u`, `ú`, `ü`	a, e, i, o, u	`canción` - k a n θ i o n
`h`	none; `hi` before a vowel at the start of a syllable is ʝ	`ahora` - a o r a, `hielo` - ʝ e l o
`ch`	tʃ	`hechizo` - e tʃ i θ o
`c` before `e`, `i`; `z`	θ	`cena` - θ e n a
`c` elsewhere, `qu` before `e`, `i`, `k`	k	`queso` - k e s o
`g` before `e`, `i`; `j`	x	`gente` - x e n t e
`g` elsewhere, `gu` before `e`, `i`	g	`guerra` - g e r a
`ll`; `y` before a vowel	ʝ	`calle` - k a ʝ e
`y` at the end of a syllable	i	`hoy` - o i
`r`, `rr`	r	`perro` - p e r o
`ñ`	ɲ	`niño` - n i ɲ o
`v`	b	`vaca` - b a k a
`x`; `x` at the start of a word	k s; s	`examen` - e k s a m e n, `xilófono` - s i l o f o n o
`w`	u	`whisky` - u i s k i

The pronunciation is the one of the standard of Spain, with yeísmo and distinción. A rule reads the spelling of a word, not its history: the x of México is k s, as in examen.

Example

from ests.phon_stats import transcribe

transcribe("hechizo"), transcribe("guerrilla"), transcribe("examen")
# (('e', 'tʃ', 'i', 'θ', 'o'), ('g', 'e', 'r', 'i', 'ʝ', 'a'),
#  ('e', 'k', 's', 'a', 'm', 'e', 'n'))

Parameter	Type	Default	Description
`word`	str	`-`	Word

CV pattern¶

ests.phon_stats.cv_pattern()

The CV pattern of a word or a syllable: the vowels are written V and the consonants C, over the sounds of the transcription - queso is CVCV, hora VCV, examen VCCVCVC. A syllable is read as a word of its own, so its initial x is s, as at the start of a word: xi is CV, while the syllable xi of México counts as CCV in PhonStats.

Parameter	Type	Default	Description
`word`	str	`-`	Word or syllable

Open syllable¶

ests.phon_stats.is_open_syllable()

Checks whether a syllable is open: an open syllable ends in a vowel sound - ca, que, hoy (the final y is the vowel i); car and pan are closed. The syllable is read as a word of its own.

Parameter	Type	Default	Description
`syllable`	str	`-`	Syllable

Consonant clusters¶

ests.phon_stats.calc_consonant_clusters()

The distribution of the consonant clusters by length. A cluster is a run of consonant sounds inside a word, across the syllables: instrumento has the clusters n s t r (4), m (1) and n t (2). The clusters of length 1 are the single consonants between vowels or at the edges of a word. The digraphs are one sound (calle, perro, chico), and x is two (extra - k s t r).

Parameter	Type	Default	Description
`text`	list[str]	`-`	List of words

Hiatuses¶

ests.phon_stats.calc_hiatus()

The number of hiatuses: two vowels next to each other in two syllables of a word, as syllabify splits them - po-e-ta, dí-a, le-er, a-é-re-o (two). A silent h between the vowels does not break the hiatus (bú-ho, a-ho-ra), and the vowels of a diphthong are one syllable and no hiatus (cie-lo, ciu-dad).

Parameter	Type	Default	Description
`text`	list[str]	`-`	List of words

Entropy of the CV patterns¶

ests.phon_stats.calc_cv_entropy()

The Shannon entropy of the distribution of the words by CV pattern in bits: the higher it is, the more varied the phonetic shape of the words of the text. Words without sounds (numbers) are left out; nan for a text without them.

\[ H = -\sum_{k} p_k \log_2 p_k \]

where \(p_k\) is the share of the words with the CV pattern \(k\).

Parameter	Type	Default	Description
`text`	list[str]	`-`	List of words

Hardness¶

The ratio of the voiceless obstruents to the vowels and the sonorants: a text of p, t, k, s and θ sounds harder than a text of vowels and m, n, l, r.

\[ \frac{n_{voiceless}}{n_{vowels} + n_{sonorants}} \]

Alliteration index¶

ests.phon_stats.calc_alliteration()

The ratio of the observed number of windows of window_len neighbouring words where one consonant sound occurs in two words or more to the number expected if the consonants were spread over the words at random, summed over the consonants. The expected number comes from the frequencies of the consonants in the text itself, so the index tells whether the repetitions cluster in neighbouring words, not how frequent a sound is: about 1 - the repetitions are random, well above 1 - alliteration. The consonants are the sounds of the transcription, so casa and queso repeat k, and cena and casa do not; nan for a text shorter than the window.

\[ \frac{\sum_c O_c}{\sum_c (W - w + 1) \left(1 - (1 - p_c)^w - w p_c (1 - p_c)^{w - 1}\right)} \]

where \(O_c\) is the number of windows with the consonant \(c\) in two words or more, \(W\) the number of words, \(w\) the window and \(p_c\) the share of the words with the consonant \(c\).

Parameter	Type	Default	Description
`text`	list[str]	`-`	List of words
`window_len`	int	`3`	Window in words

Assonance index¶

ests.phon_stats.calc_assonance()

The same ratio over the vowels: the observed number of windows of window_len neighbouring words where one vowel occurs in two words or more against the number expected by the frequencies of the vowels in the text. Every vowel counts, stressed or not; nan for a text shorter than the window.

Parameter	Type	Default	Description
`text`	list[str]	`-`	List of words
`window_len`	int	`3`	Window in words