The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguese
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917587728728064 |
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| author | Kulkarni, Ajinkya Tokareva, Anna Qureshi, Rameez Couceiro, Miguel |
| author_facet | Kulkarni, Ajinkya Tokareva, Anna Qureshi, Rameez Couceiro, Miguel |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_07513 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguese Kulkarni, Ajinkya Tokareva, Anna Qureshi, Rameez Couceiro, Miguel Computation and Language Artificial Intelligence Computers and Society 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. |
| title | The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguese |
| topic | Computation and Language Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2402.07513 |