Some consonants can take the function of the vowel in unstressed syllables. Where necessary, a syllabic marker diacritic is used, hence /ˈpɛtl/ but /ˈpɛtl̩i/.
Vowels
iːfleece
ihappy
ɪkit
ɛdress
atrap, bath
ɑːstart, palm, bath
ɒlot
ɔːthought, force
ʌstrut
ʊfoot
uːgoose
əletter
əːnurse
ɪənear
ɛːsquare
ʊəcure
eɪface
ʌɪpride
aʊmouth
əʊgoat
ɔɪvoice
ãgratin
ɒ̃salon
ᵻ(/ɪ/-/ə/)
ᵿ(/ʊ/-/ə/)
Other symbols
The symbol ˈ at the beginning of a syllable indicates that that syllable is pronounced with primary stress.
The symbol ˌ at the beginning of a syllable indicates that that syllable is pronounced with secondary stress.
Round brackets ( ) in a transcription indicate that the symbol within the brackets is optional.
Some consonants can take the function of the vowel in unstressed syllables. Where necessary, a syllabic marker diacritic is used, hence /ˈpɛd(ə)l/ but /ˈpɛdl̩i/.
Vowels
ifleece, happy
ɪkit
ɛdress
ætrap, bath
ɑlot, palm, cloth, thought
ɑrstart
ɔcloth, thought
ɔrnorth, force
ʊfoot
ugoose
əstrut, comma
ərnurse, letter
ɪ(ə)rnear
ɛ(ə)rsquare
ʊ(ə)rcure
eɪface
aɪpride
aʊmouth
oʊgoat
ɔɪvoice
ɑ̃gratin
æ̃salon
ᵻ(/ɪ/-/ə/)
ᵿ(/ʊ/-/ə/)
Other symbols
The symbol ˈ at the beginning of a syllable indicates that that syllable is pronounced with primary stress.
The symbol ˌ at the beginning of a syllable indicates that that syllable is pronounced with secondary stress.
Round brackets ( ) in a transcription indicate that the symbol within the brackets is optional.
Simple text respell breaks words into syllables, separated by a hyphen. The syllable which carries the primary stress is written in capital letters. This key covers both British and U.S. English Simple Text Respell.
Consonants
b, d, f, h, k, l, m, n, p, r, s, t, v, w and z have their standard English values
gguy
jjay
yyore
chchore
khloch
shshore
ththaw
dhthee
zhbeige
Vowels
atrap
ahpalm
airsquare
arstart
arrcarry (British only)
awthought
ayface
a(ng)gratin
edress
eefleece
eerdeer
errmerry
ikit
ighpride
irrmirror
olot (British only)
ohgoat
oogoose
oorcure
orforce
orrsorry (British only)
owmouth
oyvoice
o(ng)salon
ustrut
uhletter
urnurse
urrhurry
uufoot
Frequency
zest typically occurs about 0.02 times per million words in modern written English.
zest is in frequency band 3, which contains words occurring between 0.01 and 0.1 times per million words in modern written English. More about OED's frequency bands
Frequency data is computed programmatically, and should be regarded as an estimate.
Frequency of zest, v., 1760–2010
* Occurrences per million words in written English
Historical frequency series are derived from Google Books Ngrams (version 2), a data set based on the Google Books corpus of several million books printed in English between 1500 and 2010.
The overall frequency for a given word is calculated by summing frequencies for the main form of the word, any plural or inflected forms, and any major spelling variations.
For sets of homographs (distinct entries that share the same word-form, e.g. mole, n.¹, mole, n.², mole, n.³, etc.), we have estimated the frequency of each homograph entry as a fraction of the total Ngrams frequency for the word-form. This may result in inaccuracies.
Smoothing has been applied to series for lower-frequency words, using a moving-average algorithm. This reduces short-term fluctuations, which may be produced by variability in the content of the Google Books corpus.
Decade
Frequency per million words
1760
0.011
1770
0.012
1780
0.014
1790
0.016
1800
0.021
1810
0.024
1820
0.031
1830
0.037
1840
0.044
1850
0.049
1860
0.055
1870
0.06
1880
0.065
1890
0.069
1900
0.071
1910
0.071
1920
0.068
1930
0.062
1940
0.055
1950
0.047
1960
0.037
1970
0.031
1980
0.026
1990
0.023
2000
0.022
2010
0.022
Frequency of zest, v., 2017–2024
* Occurrences per million words in written English
Modern frequency series are derived from a corpus of 20 billion words, covering the period from 2017 to the present. The corpus is mainly compiled from online news sources, and covers all major varieties of World English.
Smoothing has been applied to series for lower-frequency words, using a moving-average algorithm. This reduces short-term fluctuations, which may be produced by variability in the content of the corpus.