AI Demo - Instructor - Teacher - Professional - Business Woman - Real - Smart - Announcer
Description
Vocal Characteristics
Language
EnglishVoice Age
Middle Aged (35-54)Accents
British (General) British (Received Pronunciation - RP, BBC) North American (General)Transcript
Note: Transcripts are generated using speech recognition software and may contain errors.
a lexicon, American and English. Can we reduce these biases? In fact, we can. Here is the output of award embedding system found on the Internet. It's in popular use by programmers. Despite the biases it's picked up from, the data said. For example, it suggests that he is brilliant. While she is sympathetic to correct it, our researchers gave the words he and she the same value which greatly reduced gender bias. By applying the same technique to job applicants, we can achieve a similar result. Gender based errors dropped for both men and women. A good start. Can we reduce thes biases? In fact, we can hear is the output of a word embedding system found on the Internet. It's in popular use by programmers. Despite the biases it's picked up from the data set, for example, it suggests that he is brilliant. While she is sympathetic to corrected, our researchers gave the words he and she the same value which greatly reduced gender bias. By applying this same technique to job applicants, we can achieve a similar result. Gender based errors drop for both men and women. A good start