I'm currently dealing with a lot of headaches trying to test phone-based AI agents across various accents. I never realized how many different accents there are until users started calling in. While the agent performs well with English from the US and Canada, it really struggles with stronger accents like Indian, Nigerian, or Eastern European. I'm curious if anyone has found a good way to evaluate the robustness of the AI in recognizing different accents instead of just waiting until we get complaints from frustrated customers.
4 Answers
Instead of relying on customer feedback, why not set up a proper call evaluation system? You could implement localized call routing to handle accents better and potentially work with human agents for those cases. Leaning solely on AI can frustrate customers more than help them, so consider a mixed approach.
I hear you about the struggles with accents! We tackled this by using controlled simulated calls, which allowed us to work with different speaker profiles. There's this platform called Cekura that has preset accent sets and various noise environments to make the testing process more consistent and reliable.
Have you checked if you have a comprehensive set of accent samples? It might be worth rebuilding your dataset to include a wider array of accents for better training. That way, the agent could learn to handle diverse calls more effectively.
I've been in this game for a while, and even I find it tough when I get strong accents on calls! It's not just the AI; the human ear can struggle too at times. Maybe consider training with actual users who speak those accents as a part of your testing process.
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