Detecting and Reducing Bias in Speech Recognition

Bias in automated speech recognition (ASR) systems has become an important issue for companies as it can impact customers negatively based on factors such as gender, race, age, and accent. Detecting bias in ASR systems involves identifying if transcripts for certain speakers have significantly higher word error rates than others. To address this issue, understanding the source of bias is crucial, which often comes from biased data used to train models. Solutions may include gathering more diverse data or adjusting the model's parameters to better handle edge cases and reduce bias.

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