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  • Racial disparities in automated speech recognition | PNAS
    Automated speech recognition (ASR) systems are now used in a variety of applications to convert spoken language to text, from virtual assistants, to closed captioning, to hands-free computing By analyzing a large corpus of sociolinguistic interviews with white and African American speakers, we demonstrate large racial disparities in the performance of five popular commercial ASR systems Our
  • Racial disparities in automated speech recognition
    Analysis of a large corpus of sociolinguistic interviews with white and African American speakers demonstrates large racial disparities in the performance of five popular commercial ASR systems, and proposes strategies to reduce these performance differences and ensure speech recognition technology is inclusive Significance Automated speech recognition (ASR) systems are now used in a variety
  • Reexamining Racial Disparities in Automatic Speech Recognition . . .
    Our findings highlight two key factors affecting the performance of state-of-the-art ASR models on AAE speech data from CORAAL One of these factors, dialectal variation, has been previously identified as a weakness in modern ASR systems (Koenecke et al , 2020; Martin and Tang, 2020) potentially contributing to the problem of racial disparity
  • Understanding Racial Disparities in Automatic Speech Recognition: The . . .
    Recent research has highlighted that state-of-the-art automatic speech recognition (ASR) systems exhibit a bias against African American speakers In this research, we investigate the underlying
  • Understanding racial disparities in automatic speech recognition: The . . .
    Summary Recent research has highlighted that state-of-the-art automatic speech recognition (ASR) systems exhibit a bias against African American speakers In this research, we investigate the underlying causes of this racially based disparity in performance, focusing on a unique morpho-syntactic feature of African American English (AAE), namely habitual "be", an invariant form of "be" that
  • “It feels like were not meeting the criteria”: Examining and . . .
    Allison Koenecke, Andrew Nam, Emily Lake, Joe Nudell, Minnie Quartey, Zion Mengesha, Connor Toups, John R Rickford, Dan Jurafsky, and Sharad Goel 2020 Racial disparities in automated speech recognition Proceedings of the National Academy of Sciences 117, 14: 7684–7689
  • Racial disparities in automated speech recognition
    Analysis of a large corpus of sociolinguistic interviews with white and African American speakers demonstrates large racial disparities in the performance of five popular commercial ASR systems, and proposes strategies to reduce these performance differences and ensure speech recognition technology is inclusive Expand View on NAS pnas org Save to Library Create Alert Cite
  • Automatic Speech Recognition of African American English: Lexical and . . .
    1 Introduction Addressing racial bias in ASR has recently become a significant area of concern Given AAE as a minority dialect, this issue was phonetically confirmed by Koenecke et al [1], who found that the average WER for white American speakers was significantly lower than that for AAE speakers across five prominent ASR systems


















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