  It is well known that many machine learning systems demonstrate bias towards
specific groups of individuals. This problem has been studied extensively in
the Facial Recognition area, but much less so in Automatic Speech Recognition
(ASR). This paper presents initial Speech Recognition results on "Casual
Conversations" -- a publicly released 846 hour corpus designed to help
researchers evaluate their computer vision and audio models for accuracy across
a diverse set of metadata, including age, gender, and skin tone. The entire
corpus has been manually transcribed, allowing for detailed ASR evaluations
across these metadata. Multiple ASR models are evaluated, including models
trained on LibriSpeech, 14,000 hour transcribed, and over 2 million hour
untranscribed social media videos. Significant differences in word error rate
across gender and skin tone are observed at times for all models. We are
releasing human transcripts from the Casual Conversations dataset to encourage
the community to develop a variety of techniques to reduce these statistical
biases.
