1. A computer-implemented method comprising: selecting, from among a set of items of uploaded content, a first candidate training subset of the items of uploaded content that (i) each has a respective textual label that includes particular text that is indicative of a presence of a sound of a human voice, and of an absence of background sounds that include sounds other than that of a human voice, and (ii) are associated with a particular entity; for each of the items of uploaded content of the first candidate training subset that (i) each has a respective textual label that includes particular text that is indicative of a presence of a sound of a human voice, and of an absence of background sounds that include sounds other than that of a human voice, and (ii) are associated with a particular entity, obtaining one or more acoustic features that are extracted from the item of uploaded content of the first candidate training subset; selecting, from among the first candidate training subset of the items of uploaded content, a second, filtered candidate training subset of the items of uploaded content that (i) each has a respective textual label that includes particular text that is indicative of a presence of a sound of a human voice, and of an absence of background sounds that include sounds other than that of a human voice, (ii) are associated with a particular entity, and (iii) each has acoustic features that are indicated as similar to acoustic features of one or more other items of uploaded content of the first subset; and using one or more respective acoustic features of one or more of the items of uploaded content of the second, filtered candidate training subset that (i) each has a respective textual label that includes particular text that is indicative of a presence of a sound of a human voice, and of an absence of background sounds that include sounds other than that of a human voice, (ii) are associated with a particular entity, and (iii) each has acoustic features that are indicated as similar to acoustic features of one or more other items of uploaded content of the first candidate training subset in recognizing whether a subsequently received audio sample is likely also associated with the particular entity.
1. A computer-implemented method comprising: selecting, from among a set of items of uploaded content, a first candidate training subset of the items of uploaded content that (i) each has a respective textual label that includes particular text that is indicative of a presence of a sound of a human voice, and of an absence of background sounds that include sounds other than that of a human voice, and (ii) are associated with a particular entity; for each of the items of uploaded content of the first candidate training subset that (i) each has a respective textual label that includes particular text that is indicative of a presence of a sound of a human voice, and of an absence of background sounds that include sounds other than that of a human voice, and (ii) are associated with a particular entity, obtaining one or more acoustic features that are extracted from the item of uploaded content of the first candidate training subset; selecting, from among the first candidate training subset of the items of uploaded content, a second, filtered candidate training subset of the items of uploaded content that (i) each has a respective textual label that includes particular text that is indicative of a presence of a sound of a human voice, and of an absence of background sounds that include sounds other than that of a human voice, (ii) are associated with a particular entity, and (iii) each has acoustic features that are indicated as similar to acoustic features of one or more other items of uploaded content of the first subset; and using one or more respective acoustic features of one or more of the items of uploaded content of the second, filtered candidate training subset that (i) each has a respective textual label that includes particular text that is indicative of a presence of a sound of a human voice, and of an absence of background sounds that include sounds other than that of a human voice, (ii) are associated with a particular entity, and (iii) each has acoustic features that are indicated as similar to acoustic features of one or more other items of uploaded content of the first candidate training subset in recognizing whether a subsequently received audio sample is likely also associated with the particular entity. 7. The method of claim 1 , wherein selecting a first candidate training subset of the items of uploaded content that are associated with a particular entity comprises selecting the items of uploaded content that each has a respective textual label that identifies the particular entity.