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.

我们提出了一种名为 SpEAT 的方法，用于检测预训练模型中的偏见；使用该方法对 16 个英语语音模型进行测试，发现预训练语音模型普遍存在人类类似的偏见，并证明这些偏见会对 Speech Emotion Recognition 任务产生实际影响。

预训练的语音处理模型含有类人的偏见并传播至语音情感识别

Pre-trained Speech Processing Models Contain Human-Like Biases that  Propagate to Speech Emotion Recognition

Generated texts from large pretrained language models have been shown to
exhibit a variety of harmful, human-like biases about various demographics.
These findings prompted large efforts aiming to understand and measure such
effects, with the goal of providing benchmarks that can guide the development
of techniques mitigating these stereotypical associations. However, as recent
research has pointed out, the current benchmarks lack a robust experimental
setup, consequently hindering the inference of meaningful conclusions from
their evaluation metrics. In this paper, we extend these arguments and
demonstrate that existing techniques and benchmarks aiming to measure
stereotypes tend to be inaccurate and consist of a high degree of experimental
noise that severely limits the knowledge we can gain from benchmarking language
models based on them. Accordingly, we propose a new framework for robustly
measuring and quantifying biases exhibited by generative language models.
Finally, we use this framework to investigate GPT-3's occupational gender bias
and propose prompting techniques for mitigating these biases without the need
for fine-tuning.

本文研究了预训练语言模型的生成文本中存在的人类偏见和其对不同人口群体的影响，针对现有的评估技术和基准的准确性问题，提出了新的评估框架以更稳健地测量和量化语言模型所表现出的偏见。并使用该框架调查了 GPT-3 的职业性别偏见，并提出了一些缓解这些偏见的提示技术。