Saturday 05 April 2025
The quest for a more inclusive artificial intelligence has led researchers to tackle a crucial yet often overlooked aspect: bias in automatic speech recognition systems. A recent study published in ArXiv delves into the dual challenges of bias and sustainability in ASR, highlighting the disparities in performance across gender, accent, and age groups.
To investigate these biases, the authors employed two datasets: Artie-Bias, a read-speech dataset featuring 1712 utterances with demographic information on speakers’ gender, age, and accent; and CCv2, a spontaneous speech dataset comprising 5567 unique speakers from diverse backgrounds. The analysis revealed significant disparities in performance across both datasets, particularly for female speakers, non-native English speakers, and older adults.
The study also explored the carbon footprint of ASR systems during inference, using three widely used platforms: codecarbon, carbontracker, and eco2ai. Results showed that MMS (Massively Multilingual Speech) outperformed Whisper variants in terms of sustainability, with NVIDIA GPU A100-40GB exhibiting better energy consumption and carbon emissions than other GPUs.
The authors’ findings have far-reaching implications for the development and deployment of ASR systems in various applications, from voice assistants to emergency services. The study underscores the need for more inclusive and sustainable AI solutions that cater to diverse user populations.
In their analysis, the researchers identified several factors contributing to bias in ASR systems. For instance, they found that larger variants of Whisper performed worse than medium-sized models, suggesting that overparameterization can exacerbate biases. Additionally, the study highlighted the importance of language-specific adapters in reducing emissions and mitigating biases.
The authors’ work has significant implications for policymakers, developers, and users alike. As AI becomes increasingly integral to our daily lives, it is essential to ensure that these systems are not only accurate but also equitable and environmentally responsible.
The study’s findings have sparked a much-needed conversation around the dual challenges of bias and sustainability in ASR. By shedding light on these crucial issues, researchers can work towards creating more inclusive and sustainable AI solutions that benefit all users.
Cite this article: “Unveiling Biases and Footprints: A Comprehensive Study on Automatic Speech Recognition Systems”, The Science Archive, 2025.
Artificial Intelligence, Automatic Speech Recognition, Bias, Sustainability, Inclusivity, Natural Language Processing, Machine Learning, Carbon Footprint, Environmental Impact, Ai Ethics
