1. A text conversion method, comprising: acquiring a target spoken text that comprises a non-spoken morpheme and a spoken morpheme, wherein the spoken morpheme is a type of morpheme that has one or more spoken morpheme characteristics, and wherein the spoken morpheme characteristics comprise an inserted morpheme, a repeated morpheme, and an amending morpheme; determining an initial weighted finite-state transducer (WFST) model database according to a text training database by means of statistical learning, wherein the initial WFST model database comprises N initial spoken WFST models corresponding to N spoken texts, wherein the N initial spoken WFST models include first states, wherein each spoken text of the N spoken texts comprises the type of morpheme, and wherein an output of each state of the first states that corresponds to the type of morpheme is not empty; determining a spoken morpheme characteristic WFST model database according to a spoken morpheme training database and the spoken morpheme characteristics and by means of statistical learning, wherein an output of each state in the spoken morpheme characteristic WFST model database that corresponds to the type of morpheme is empty; modifying the N initial spoken WFST models in the initial WFST model database according to the spoken morpheme characteristic WFST model database to determine N modified spoken WFST models, wherein an output of each state of the N modified WFST models that corresponds to the type of morpheme is empty; determining a target WFST model database, wherein the target WFST model database comprises the N modified spoken WFST models; determining, from the target WFST model database, a target WFST model corresponding to the target spoken text, wherein an output of each state in the target WFST model that corresponds to the type of morpheme is empty, and wherein, for each state in the target WFST model that corresponds to the type of morpheme, an output of the state is the same as an input of the state; and converting the target spoken text into a written text corresponding to the target spoken text by processing the target spoken text using the target WFST model, wherein the written text comprises the non-spoken morpheme and does not comprise the spoken morpheme.