![]() Similarly, I imagine how limiting these tools could be for women. Not a world I want to imagine for myself at least not with the current ASR tools. “Now imagine, if I had a handicap and all I could use was a voice interface. I would love to use voice interfaces but can't most of the time. To speak for myself, I feel frustrated most ASR systems fail terribly with Indian accents. Imagine a big chunk of the demographic being cut access to a product because of their gender or ethnicity. ![]() Rao told El Reg this week: “The real-world impacts are as you can imagine significant. Many speech recognition systems are tailored towards Western accents. More worryingly, this also applies to things like race and ethnicity, where there isn’t an acoustic reason for one group to be harder to understand,” Tatman said. And if you've trained your system on data from 90 per cent men and 10 per cent women (unlikely but possible, especially if you're not accounting for gender in your training data), you'll end up being very good at recognizing male data and very bad at recognizing female data. ![]() “Deep learning, in particular, is very good at recognizing things that it's seen a lot of. Since voice recognition systems already find it more difficult to cope with female voices, the problem of gender biases could get worse if systems learn from unbalanced training datasets.
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