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- W4381245672 abstract "Second-pass rescoring is employed in most state-of-the-art speech recognition systems. Recently, BERT based models have gained popularity for re-ranking the n-best hypothesis by exploiting the knowledge from masked language model pre-training. Further, fine-tuning with discriminative loss such as minimum word error rate (MWER) has shown to perform better than likelihood-based loss. Streaming applications with low latency requirements impose significant constraints on the size of the models, thereby limiting the word error rate (WER) performance gains. In this paper, we propose effective strategies for distilling from large models discriminatively trained with the MWER objective. We experiment on Librispeech and production scale internal dataset for voice-assistant. Our results demonstrate relative improvements of upto 7% WER over student models trained with MWER. We also show that the proposed distillation can reduce the WER gap between the student and the teacher by 62% upto 100%." @default.
- W4381245672 created "2023-06-20" @default.
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- W4381245672 date "2023-06-15" @default.
- W4381245672 modified "2023-09-23" @default.
- W4381245672 title "Distillation Strategies for Discriminative Speech Recognition Rescoring" @default.
- W4381245672 doi "https://doi.org/10.48550/arxiv.2306.09452" @default.
- W4381245672 hasPublicationYear "2023" @default.
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