The Balancing Act: Unmasking and Alleviating ASR Biases in P ortuguese (2024)

Abstract: In the field of spoken language understanding, systems like Whisper and Multilingual Massive Speech (MMS) have shown state-of-the-art performances. This study is dedicated to a comprehensive exploration of the Whisper and MMS systems, with a focus on assessing biases in automatic speech recognition (ASR) inherent to casual conversation speech specific to the Portuguese language. Our investigation encompasses various categories, including gender, age, skin tone color, and geo-location. Alongside traditional ASR evaluation metrics such as Word Error Rate (WER), we have incorporated p-value statistical significance for gender bias analysis. Furthermore, we extensively examine the impact of data distribution and empirically show that oversampling techniques alleviate such stereotypical biases. This research represents a pioneering effort in quantifying biases in the Portuguese language context through the application of MMS and Whisper, contributing to a better understanding of ASR systems’ performance in multilingual settings.

## introduction conversational artificial intelligence (ai) has become increasingly integrated into everyday applications over the past few years. the history of previous broad technologies shows that despite temporary challenges, restructuring the economy around innovative technologies offers significant long-term benefits @xcite . this asks for fair ai solutions that can connect people from different backgrounds, and that enables universal access to technology. in the context of human-machine interactions through spoken language, automatic speech recognition (asr) facilitates smooth information exchange within various conversational ai applications, including machine translation, sentiment analysis, and question-answering systems @xcite . the significance of spoken language in our daily lives emphasizes the need for asr systems to accommodate the various forms of human communication. it is thus vital that asr systems can adeptly manage this diversity, as it is crucial for enabling smooth and inclusive communication across a wide range of situations and people, and extending the use of asrs in domains such as emergency services, home automation, and navigation systems. to accommodate fairness and transparency requirements it is paramount to examine the prevailing biases within various subgroups towards fair asr systems. over the past few years, there has been a growing research community examining biases in automatic speech recognition (asr) systems @xcite @xcite @xcite . this research has primarily focused on assessing the impact of disparities related to gender, age, accent, dialect, and racial meta-attributes. (it is worth mentioning that most of these features are considered sensitive according to legal protection against discrimination, e.g., in the u.s. foot_0 and in europe foot_1 .) however, the majority of these studies have been carried out on monolingual asr systems for the english language, with only a limited number of studies addressing bias detection in non-english languages. in the study conducted in @xcite , researchers examined the (hidden markov model) hmm-deep neural network (dnn) asr system to assess biases related to gender, age, and accents in the context of the dutch language. they then proposed the use of data augmentation and vocal tract length normalization techniques to alleviate these biases in dutch asr systems @xcite . another study centered on french broadcasting speech, aimed to uncover gender biases and revealed that the underrepresentation of specific gender categories could result in bias in hmm-dnn asr performance, regardless of gender identity (male, female, or other) @xcite . furthermore, it emphasized the importance of a systematic examination of demographic imbalances present in datasets. for arabic asr system, which were developed using carnegie mellon university sphinx 3 tools 3 , an investigation was conducted to understand the impact of gender, age, and regional factors on performance @xcite . while these studies laid the foundation for quantifying biases, there remains a scarcity of research on asr systems trained with large amounts of multilingual data, even though they consistently achieve stateof-the-art performance levels. the emergence of computational resources enabled the acceleration of the development of large pre-trained acoustic models, resulting in unified frameworks with multilingual capabilities. these frameworks are often built upon transformer networks and prominently use the wav2vec 2.0 @xcite framework. as a consequence, there has been a significant push to create multilingual asr systems @xcite @xcite , extending their applicability to more than 100 languages, including those with limited linguistic resources. meta ai's mms system @xcite conducted an evaluation that included the assessment of gender and language biases using the fleurs dataset @xcite . however, there is still a need for a deeper understanding of the comparative differences among various multilingual asr systems when it comes to quantifying potential biases. to explore the biases present in multilingual asr systems trained on extensive speech data, we investigated variants of openai's whisper asr system @xcite and meta ai's mms asr system @xcite , both of which have achieved state-of-the-art performance levels. in addition, we selected the casual conversation dataset version 2 (ccd v2) to quantify biases and assess the fairness of these system performances 3 https://www.cs.cmu.edu/~archan/sphinxinfo. html in the context of the portuguese language @xcite . our study takes into account a diverse spectrum of categories, including age groups, gender, geographical locations, and skin tones. the consistency in textual content across all ccd v2 recordings establishes a robust basis for the efficient evaluation of system performance across a broad array of categories. only a limited number of studies have delved into the influence of stateof-the-art multilingual asr systems on domainspecific asr tasks. for example, these studies have explored code-switching between languages using systems like whisper and mms @xcite , or they have examined the effects of asr errors on discourse models among groups of students in noisy, real-world classroom settings between whisper and google asr system @xcite . more often, an imbalanced distribution of evaluation data across various sub-categories can result in an inadequate analysis of the evaluation process itself. therefore, we explore two resampling methods, namely, naïve and synthetic minority oversampling technique (smote) @xcite , to ensure a balanced data distribution across each subgroup when quantifying the biases. in the assessment of asr systems, our primary choice of metrics includes word error rate (wer) and character error rate (cer) 4 . interestingly, we observe that oversampling techniques can alleviate performance disparities between certain subgroups. the structure of the paper is as follows. in section 2, we provide an overview of the casual conversation dataset, which is utilized to quantify biases in multilingual asr systems in the portuguese language. we described the specifics of the mms asr system and the variants of whisper asr systems along with the evaluation protocol in section 3. we outline results along with an analysis on various categories to to quantify biases in section 4, along with the corresponding evaluation methodologies. section 5 details the discussion, and we draw our conclusions in section 6 along with potential directions for future work. the main contributions of this paper are as follows: ## dataset description the ccd v2 dataset is open-source and can be accessed through the meta ai website foot_3 . it represents the speech of 5,567 unique speakers from various regions, including india, the united states of america, indonesia, vietnam, brazil, mexico, and the philippines. this compilation results in five audio samples per individual, yielding a total of 26,467 video recordings. the dataset encompasses seven self-labeled attributes, including details about the speaker's age, gender, native and secondary languages or dialects, disabilities, physical characteristics, and adornments, as well as geographic location. additionally, it features four other characteristics: two skin tone scales @xcite and fitzpatrick skin type @xcite ), voice timbre, the speaker's activity, categorized as gesture, action, or appearance, and details about the recording setup, which covers video quality, background environment, and video configuration. for monk skin tone scale-10 only one sample was available for portuguese language. therefore, in order to avoid skewed comparison between skin-tone scales using monk skin tone, we only conducted a study using fitzpatrick skin type. the ccd v2 comprises 354 hours of recordings where speakers responded to specific questions in a non-scripted manner and 319 hours of recordings in which individuals read passages from f. dostoyevsky's "the idiot", translated into various languages. throughout this paper, we utilized scripted recordings for the portuguese language. as each scripted recording had the same textual content and phonetic variations, it enables the examination of meta-attributes leading to performance differences. for more comprehensive details of ccd v2 and the dataset design process, please refer to the works published in @xcite and @xcite . in the context of assessing the fairness of asr systems, we focused primarily on a subset of scripted recordings, with a strong emphasis on the portuguese language. in this study, we concentrated on four annotated labels: gender, age, fitzpatrick scale, and geographic location. to simplify our analysis, we categorized speakers into seven age groups: 18-24, 25-30, 31-36, 37-42, 43-50, 51-60, and 61+. after the initial analysis of the evaluation sample distribution for each sub-category, we observed imbalanced distributions among various subgroups. we thus explored resampling strategies to ensure that biases are not introduced into the computed results due to imbalanced distributions across subgroups. ## empirical study in this empirical study, we initiate our investigation by conducting a thorough analysis of the influence of various sampling techniques on performance disparities within multilingual asr systems for portuguese. additionally, in section 3.1, we first present the asr systems employed in this research. subsequently, we outline the evaluation protocol and data preparation in section 3.2. ## results and analysis in this section, we present a comprehensive analysis of word error rate (wer) within distinct categories as provided by ccd v2. these categories include gender (section 4.1), skin tone (section 4.2), age groups (section 4.3), and geo-location (section 4.4). as previously mentioned, our experimentation involved the use of three whisper asr variants: medium (769 million parameters), large (1550 million parameters), and large-v2 (1550 million parameters) foot_5 (which maintains the same parameter count but benefits from extended training with regularization). additionally, we utilized the mms asr system foot_6 with 965 million parameters. ## discussion and limitations our results reveal that all 4 models show mild wer performance disparities when considering the individual subgroups of the categories 'gender', 'age', 'skin tone color', and 'geo-location', with a consistently better performance of the mms model over the three whisper models. however, when analyzing the gendered subcategories of 'age', 'skin tone color', and 'geo-location', we observe significant differences in wer, with a noticeable bias that privileges the 'male' subgroup; see additional results in https://biasinai.github.io/ asrbias/ . our study also shows that oversampling approaches can alleviate these disparities between the two gender subgroups. this is particularly evident in figure 2 , where wer performances are balanced for the 'male' and 'female' subgroups over the 4 models considered. the same trend was also observed for the other gendered categories and with respect to the character error rate (cer) in the link provided earlier. the study shows that performances of whisper variants demonstrate higher sensitivity to the number of model parameters, whereas the mms system, despite having 40% fewer parameters than whisper large, exhibits better robustness over the various categories. despite promising, these results naturally ask for similar comparisons with respect to other performance and bias metrics. another limitation of our study is that it was carried out solely on the ccd v2. in @xcite , the artie bias corpus is curated as a subset of the mozilla common voice corpus. it includes demographic tags for age, gender, and accent, which allows for the examination of disparities in the english language. it is imperative to construct bias-focused datasets using publically available resources for portuguese. furthermore, we can also extend this investigation to other state-of-the-art multilingual asr systems such as universal speech model @xcite , asr2k @xcite and on other tasks (e.g., speaker verification @xcite ). also, we only experimented with the original smote @xcite framework, but improvements could be obtained with dedicated versions, e.g., (alex and nayahi, 2023), @xcite , @xcite . our study focused on the portuguese language but we are currently extending it to other languages. finally, these results ask for a thorough analysis to detect the speech meta-features that trigger the disparate behavior of these asr systems. for instance, correlational features among skin-tone scale and voicetimber in speech utterances affect the disparity gap in performance. ## conclusion in this work, we presented an extensive study of recent asr systems, namely, whisper and mms, in the light of stereotypical biases such as gender, age, skin tone, and geo-location, for the portuguese language. despite observing mild performance disparities concerning individual categories such as 'age', 'skin tone color', and 'geo-location', we empirically show significant performance differences between the 'male' and 'female' subgroups. the first observation was to notice the imbalance in the various distributions, and that a naïve oversampling may further contribute to disparate performance behavior. this motivated us to employ smote, and our results attested that oversampling technique has an overall beneficial impact in reducing performance differences. we also discuss some limitations of our study along with future work.