Abstract: A technique translates speech in a first language to text in a second language manner in a manner that is appropriate for the gender of the speaker. In some implementations, the technique receives an input setting that specifies one of three translation modes: masculine mode, feminine mode, and auto mode. The first two modes produce translations in masculine and feminine modes by default, respectively, while the auto mode produces translations in forms that are based on the detected characteristics of audio signals. According to some implementations, the technique uses a training framework that automatically converts a corpus of training examples that exhibit gender bias (e.g., a male gender bias) to training examples having a reducing incidence of gender bias. In some implementations, the training framework updates weights of the machine-trained model based on a combination of two loss components: translation loss and gender loss.

Abstract: A technique translates speech in a first language to text in a second language manner in a manner that is appropriate for the gender of the speaker. In some implementations, the technique receives an input setting that specifies one of three translation modes: masculine mode, feminine mode, and auto mode. The first two modes produce translations in masculine and feminine modes by default, respectively, while the auto mode produces translations in forms that are based on the detected characteristics of audio signals. According to some implementations, the technique uses a training framework that automatically converts a corpus of training examples that exhibit gender bias (e.g., a male gender bias) to training examples having a reducing incidence of gender bias. In some implementations, the training framework updates weights of the machine-trained model based on a combination of two loss components: translation loss and gender loss.