  Previous work has established that a person's demographics and speech style
affect how well speech processing models perform for them. But where does this
bias come from? In this work, we present the Speech Embedding Association Test
(SpEAT), a method for detecting bias in one type of model used for many speech
tasks: pre-trained models. The SpEAT is inspired by word embedding association
tests in natural language processing, which quantify intrinsic bias in a
model's representations of different concepts, such as race or valence
(something's pleasantness or unpleasantness) and capture the extent to which a
model trained on large-scale socio-cultural data has learned human-like biases.
Using the SpEAT, we test for six types of bias in 16 English speech models
(including 4 models also trained on multilingual data), which come from the
wav2vec 2.0, HuBERT, WavLM, and Whisper model families. We find that 14 or more
models reveal positive valence (pleasantness) associations with abled people
over disabled people, with European-Americans over African-Americans, with
females over males, with U.S. accented speakers over non-U.S. accented
speakers, and with younger people over older people. Beyond establishing that
pre-trained speech models contain these biases, we also show that they can have
real world effects. We compare biases found in pre-trained models to biases in
downstream models adapted to the task of Speech Emotion Recognition (SER) and
find that in 66 of the 96 tests performed (69%), the group that is more
associated with positive valence as indicated by the SpEAT also tends to be
predicted as speaking with higher valence by the downstream model. Our work
provides evidence that, like text and image-based models, pre-trained speech
based-models frequently learn human-like biases. Our work also shows that bias
found in pre-trained models can propagate to the downstream task of SER.
