Voice-operated assistants and voice recognition technologies often struggle with recognizing voices that do not conform to the male-female binary or have higher pitches, as seen traditionally in women and non-binary individuals. This bias roots from initial training data sets that lacked diversity, impacting effective usability for a significant user segment.
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