Fine-tuning a large speech translation model on GPT-4-reformulated gender-balanced training data raises MuST-SHE feminine-form accuracy from about 10% to over 84% without BLEU loss.
Im- proving rnn transducer modeling for end-to-end speech recognition,
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Addressing speaker gender bias in large scale speech translation systems
Fine-tuning a large speech translation model on GPT-4-reformulated gender-balanced training data raises MuST-SHE feminine-form accuracy from about 10% to over 84% without BLEU loss.