Group Fairness in Multilingual Speech Recognition Models (2024)

Abstract: We evaluate the performance disparity of the Whisper and MMS families of ASR models across the VoxPopuli and Common Voice multilingual datasets, with an eye toward intersectionality. Our two most important findings are that model size, surprisingly, correlates logarithmically with worst-case performance disparities, meaning that larger (and better) models are less fair. We also observe the importance of intersectionality. In particular, models often exhibit significant performance disparity across binary gender for adolescents.

## introduction automatic speech recognition (asr) has improved greatly, largely due to representation learning from raw audio. data scarcity is no longer a major bottleneck for many of the world's languages, and high-quality speech recognition models become more and more integrated in both our private and public lives: from automatically transcribing court proceedings or doctor's notes, to extracting speech from police patrolling, meetings or for hearing aids, speech recognition models have the potential to ease many of the mundane but important tasks we perform on a daily basis. performance of asr models has been shown to vary substantially across user groups @xcite @xcite . partial mitigation of performance disparities across user groups is sometimes possible, through distributionally robust optimization @xcite or spectral decoupling @xcite , for example, but is computationally expensive and requires large amounts of data annotated with demographic information, e.g., protected attributes of speakers. in this study, we show how small amounts of such data can be used to evaluate performance disparities, benchmarking two state-of-the-art asr architectures across languages and demographics. our purpose is twofold; (i) we want to know what the performance is for protected groups across a variety of speech models; and (ii) we want to create a baseline for other asr models to compare against. we believe (i) is extremely important, because of the large-scale impact of these models on our everyday lives and the societal imbalances they can reinforce. establishing a practice around fairness evaluation is important also for future generations of asr to ensure that benefits are equally distributed across user groups. protected attributes protected attributes refer to demographic characteristics of individuals such as race, gender, age, and religion, which are considered protected from being used as a basis for discrimination or bias in decision-making processes. in the context of data and machine learning, the consideration of protected attributes becomes crucial to ensure fairness and prevent biases in automated decision-making systems. if an asr system is not trained to handle linguistic variation, the system may exhibit much higher error rates for individuals with certain protected attributes -especially if these are correlated with particular accents, dialects, or speech patterns, as is the case of african-american vernacular english. this can disproportionately impact individuals from specific linguistic or cultural backgrounds, leading to unfair outcomes and reduced accessibility for those groups and a perpetuation of existing societal biases and discrimination. intersectionality intersectionality, as coined by kimberlé crenshaw @xcite , illuminates the intricate interconnections among multiple social identities and their role in shaping individuals' experiences and social inequalities. this concept acknowledges that oppression, discrimination, and privilege operate in multidimensional and overlapping ways, defying understanding through singular identity categories. for instance, a woman of color may encounter distinct forms of discrimination differing from those faced by a white woman or a man of color. the understanding of intersectionality posits that individuals possess a myriad of social identities simultaneously, spanning race, gender, class, sexuality, disability, and more. these identities don't exist in isolation but rather intersect and interact, producing unique challenges and experiences. furthermore, intersectionality challenges the simplistic notion that social categories operate independently, highlighting instead the complex interplay between identities. it underscores the interconnected systems of power based on social identities and acknowledges that individuals' experiences of oppression are influenced by the intersections of these identities. in essence, intersectionality offers a comprehensive framework for comprehending the complexities of identity-based discrimination and privilege, urging for a holistic approach that considers the intertwined systems of power and discrimination. this lens is particularly valuable in examining disparities in ai-driven discrimination or inclusion concerning language use by different social groups. in the following, we present the data and asr models we consider for investigating performance disparity between the binary genders and the intersectionality of age and binary gender. ## datasets we make use of two multilingual, open source datasets to evaluate the performance disparity of the two families of asr models with respect to gender fairness and intersectionality in gender and age. common voice foot_0 is a crowdsourced, continuously developed dataset covering over 200 languages and voxpopuli foot_1 is a collection of speeches given in the european parliament between 2009-2020. both datasets contain demographic information about the speakers; commonvoice has gender and age annotations, voxpopuli has gender markings as well as timestamps for each utterance. ## results we evaluate all models in the whisper family (of different sizes) and all models in the mms family (of different training data) across all demographics in all languages in our two datasets. this is a total of 651 experiments. we then run significance test on all combinations of language, dataset, model, and model size (for the whisper family). we find significant disparity in performance between the binary genders in 29% of the cases (11% of these negatively for women, 17% for men). performance disparity is prevalent across languages and across models, and it seems that model size correlates positively with such disparity (figure 1 ). here, we plot the results with model size on the x-axis, and relative disparity difference on the y-axis. we see that there is a positive, logarithmic correlation between the two variables. figure 3 shows how gender disparities are particularly high for younger speakers who identify as men. these results showcase how inferring a model's fairness from its parity on data from one demographic group, e.g. adult users, is insufficient. ## discussion mitigation some researchers have reported on attempts to make asr systems less disparate. boito et al. (2022) report that training asr models for specific demographic groups did not reduce performance disparity. such strategies also have trouble scaling in light of intersectionality. @xcite propose conditioning on cluster ids with clusters being proxies for demographic groups, but their approach is not easily integrated in pretrained asr models such as whisper and mms. dheram et al. (2022) preliminary investigations have revealed that enforcing static fairness constraints in dynamic systems can lead to inequitable data distributions and, in some cases, exacerbate existing biases @xcite . furthermore, the emergence of powerful large-scale generative models has brought to the forefront the necessity of comprehending fairness within evolving systems. the widespread deployment and versatile capabilities of these models raise a crucial question: how can we assess these models for fairness and effectively mitigate observed biases from a long-term perspective? as a small step in what we take to be the right direction, we also examined how the performance disparities of whisper and mms evolve over time. since the models are trained on data from the entire period (2009-2017), our protocol does not simulate evaluation on future data, only variance across time. see figure 2 for an overview. we see a small effect as we depart from the period's average, but with high general variance. the smallest disparities are observed in 2010, 2013, and 2015. since the voxpopuli is a collection of speeches from the european parliament, it is likely that we would see larger variance in datasets from less formal settings. we encourage other researchers to seek out or develop new datasets that can give insights into the variance in performance over time. potential implications the consistent performance gaps observed among demographic groups present a significant challenge for the practical application of asr models in real-world scenarios. as transcription services for administrative tasks, customer service voicebots, and subtitle creation for recommender systems become ubiquitous across various domains, the disparity in performance across demographics, as demonstrated in this paper, results in certain user groups being underserved. this can consequently lead to users abandoning the service altogether or impose an unjust burden, such as additional manual administration in healthcare, on those belonging to the disadvantaged demographic group. parate performance, on the other hand, can increase user retention and ameliorate discrimination in the workplace or in access to information. ## conclusion we highlight the potential social impact of asr's performance disparities across demographic groups in the -to our knowledge-first study of its kind. we run a total of 651 experiments evaluating state-ofthe-art model families on data containing protected attributes, namely binary gender and age. we release the curated dataset to ease implementation of disparity testing for researchers and developers. our main findings were as follows: (i) larger models surprisingly exhibit more performance disparity. (ii) intersectional effects are evident, largely affecting the younger speakers who identify as men. (iii) finally, we see small signs of temporal variation in disparity figures, but less dramatic than the variation observed across protected attributes. future directions our examination of performance disparities among demographic groups in asr systems represents an initial exploration of a technology increasingly relied upon across various sectors and applications worldwide. we anticipate that numerous similar investigations will ensue, as numerous questions regarding differential performance among groups remain unanswered. while awaiting longitudinal data to fully grasp the implications of asr performance on discrimination and racism beyond the system itself, we urge researchers and developers to prioritize examining performance for children. this demographic, often underrepresented in research yet overrepresented in platforms like social media, is unique in that they are learning language use while engaging with asr systems, unlike previous generations. ensuring optimal performance for this demographic is of utmost importance.