Conformer transducer achieves new state-of-the-art end-to-end (E2E) system performance and has become increasingly appealing for production. In this paper, we study how to effectively perform rapid speaker adaptation in a conformer transducer and how it compares with the RNN transducer. We hierarchically decompose the conformer transducer and compare adapting each component through fine-tuning. Among various interesting observations, there are three distinct findings: First, adapting the self-attention can achieve more than 80% gain of the full network adaptation. When the adaptation data is extremely scarce, attention is all you need to adapt. Second, within the self-attention, adapting the value projection significantly outperforms adapting the key or the query projection. Lastly, bias adaptation, despite of its compact parameter space, is surprisingly effective. We conduct experiments on a state-ofthe- art conformer transducer for an email dictation task. With 3 to 5 min source speech and 200 minute augmented personalized TTS speech, the best performing encoder and joint network adaptation yields 38.37% and 19.90% relative word error rate (WER) reduction. Combining the attention and bias adaptation can achieve 90% of the gain with significantly smaller footprint. Further comparison with the RNN transducer suggests that the new state-of-the-art conformer transducer can benefit as much as if not more from personalization.<\/p>\n","protected":false},"excerpt":{"rendered":"
Conformer transducer achieves new state-of-the-art end-to-end (E2E) system performance and has become increasingly appealing for production. In this paper, we study how to effectively perform rapid speaker adaptation in a conformer transducer and how it compares with the RNN transducer. We hierarchically decompose the conformer transducer and compare adapting each component through fine-tuning. 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