the above transcription of would is a detailed (narrow) transcription according to the rules of the International Phonetic Association; you can find a description of each symbol by clicking the phoneme buttons in the secction below.
would is pronounced in one syllable
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video examples of would pronunciation
An example use of would in a speech by a native speaker of american english:
“… at what it is and i would not call it in …”
meanings of would
Used to give a conditional or potential "softening" to the present; might, might wish.
Used with bare infinitive to form the "anterior future", indicating a futurity relative to a past time.
Wished, desired (something).
Used to impart a sense of hesitancy or uncertainty to the present; might be inclined to. Now sometimes colloquially with ironic effect.
(heading) As a modal verb, the subjunctive of will.
(heading) As a past-tense form of will.
Used with ellipsis of the infinitive verb, or postponement to a relative clause, in various senses.
Used as the auxiliary of the simple conditional modality (with a bare infinitive); indicating an action or state that is conditional on another.
Might wish ( + verb in past subjunctive); often used in the first person (with or without that) in the sense of "if only".
Used interrogatively to express a polite request; are (you) willing to …?.
Wanted to ( + bare infinitive).
Might desire; wish (something).
Used to; was or were habitually accustomed to ( + bare infinitive); indicating an action in the past that happened repeatedly or commonly.
Was determined to; loosely, could naturally have been expected to (given the tendencies of someone's character etc.).
Something that would happen, or would be the case, under different circumstances; a potentiality.
would frequency in english - A1 level of CEFR
the word would occurs in english on average 2156.7 times per one million words; this frequency warrants it to be in the study list for A1 level of language mastery according to CEFR, the Common European Framework of Reference.
topics would can be related to
it is hard to perfectly classify words into specific topics since each word can have many context of its use, but our machine-learning models believe that would can be often used in the following areas: