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The scientific community is increasingly aware of the necessity to embracepluralism and consistently represent major and minor social groups. Currently,there are no standard evaluation techniques for different types of biases.Accordingly, there is an urgent need to provide evaluation sets and protocolsto measure existing biases in our automatic systems. Evaluating the biasesshould be an essential step towards mitigating them in the systems.  This paper introduces WinoST, a new freely available challenge set forevaluating gender bias in speech translation. WinoST is the speech version ofWinoMT which is a MT challenge set and both follow an evaluation protocol tomeasure gender accuracy. Using a state-of-the-art end-to-end speech translationsystem, we report the gender bias evaluation on four language pairs and we showthat gender accuracy in speech translation is more than 23% lower than in MT.

Evaluating Gender Bias in Speech Translation