Center for Computer Research in Music and Acoustics, Stanford University, Stanford, California 94305, USA.
Department of Otolaryngology-Head and Neck Surgery, Stanford University, Stanford, California 94305,
JASA Express Lett. 2024 Sep 1;4(9). doi: 10.1121/10.0028612.
A test is proposed to characterize the performance of speech recognition systems. The QuickSIN test is used by audiologists to measure the ability of humans to recognize continuous speech in noise. This test yields the signal-to-noise ratio at which individuals can correctly recognize 50% of the keywords in low-context sentences. It is argued that a metric for automatic speech recognizers will ground the performance of automatic speech-in-noise recognizers to human abilities. Here, it is demonstrated that the performance of modern recognizers, built using millions of hours of unsupervised training data, is anywhere from normal to mildly impaired in noise compared to human participants.
提出了一种用于评估语音识别系统性能的测试方法。听力学家使用 QuickSIN 测试来衡量人类在噪声中识别连续语音的能力。该测试得出了个体能够正确识别低语境句子中 50%关键词的信噪比。有人认为，自动语音识别器的度量标准将使自动语音在噪声中的识别性能与人类能力相联系。在这里，证明了使用数百万小时的无监督训练数据构建的现代识别器在噪声中的性能与人类参与者相比，从正常到轻度受损不等。
