Startup harnesses self-supervised learning to tackle speech recognition biases

Speech recognition systems struggle to understand African American Vernacular English (AAVE). In a 2020 study by Stanford University researchers, the software performed so poorly for AAVE that some leading systems made correct transcriptions for barely half the words spoken. The researchers speculated that the systems had a common flaw: “insufficient audio data from Black speakers when training the models.” A startup called Speechmatics has developed a technique that appears to reduce this data gap. The company announced last week that its software had “an overall accuracy of 82.8% for African American voices” based on datasets used in the Stanford study.…

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