  The scientific community is increasingly aware of the necessity to embrace
pluralism 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 protocols
to measure existing biases in our automatic systems. Evaluating the biases
should be an essential step towards mitigating them in the systems.
  This paper introduces WinoST, a new freely available challenge set for
evaluating gender bias in speech translation. WinoST is the speech version of
WinoMT which is a MT challenge set and both follow an evaluation protocol to
measure gender accuracy. Using a state-of-the-art end-to-end speech translation
system, we report the gender bias evaluation on four language pairs and we show
that gender accuracy in speech translation is more than 23% lower than in MT.

Computation and Language