🔁 Using AI to explore the future of news audio

Tim Olson of KQED discusses the challenges in machine learning errors in transcripts and their partnership with Google:

When named entities aren’t understood, machine learning models make their best estimation of what was said. For example, in our test, “The Asia Foundation” was incorrectly transcribed as “age of Foundations” and “misgendered” was incorrectly transcribed as “Miss Gendered.”  For news publishers, these are not just transcription errors, but editorial problems that change the meaning of a topic and can cause embarrassment for the news outlet. This means people have to go in and correct these transcriptions, which is expensive to do for every audio segment.