Addressing speaker gender bias in large scale speech translation systems

• URL: http://arxiv.org/abs/2501.05989v1
• Date: Fri, 10 Jan 2025 14:20:46 GMT
• Title: Addressing speaker gender bias in large scale speech translation systems
• Authors: Shubham Bansal, Vikas Joshi, Harveen Chadha, Rupeshkumar Mehta, Jinyu Li,
• Abstract summary: This study addresses the issue of speaker gender bias in Speech Translation (ST) systems. We employ Large Language Models (LLMs) to rectify translations based on the speaker's gender in a cost-effective manner. We demonstrate a 70% improvement in translations for female speakers compared to our baseline and other large-scale ST systems.
• Score: 20.698663542717544
• License:
• Abstract: This study addresses the issue of speaker gender bias in Speech Translation (ST) systems, which can lead to offensive and inaccurate translations. The masculine bias often found in large-scale ST systems is typically perpetuated through training data derived from Machine Translation (MT) systems. Our approach involves two key steps. First, we employ Large Language Models (LLMs) to rectify translations based on the speaker's gender in a cost-effective manner. Second, we fine-tune the ST model with the corrected data, enabling the model to generate gender-specific translations directly from audio cues, without the need for explicit gender input. Additionally, we propose a three-mode fine-tuned model for scenarios where the speaker's gender is either predefined or should not be inferred from speech cues. We demonstrate a 70% improvement in translations for female speakers compared to our baseline and other large-scale ST systems, such as Seamless M4T and Canary, on the MuST-SHE test set.

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