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Found 12 papers, 0 papers with code
Improving N-gram Language Models with Pre-trained Deep Transformer
Although n-gram language models (LMs) have been outperformed by the state-of-the-art neural LMs, they are still widely used in speech recognition due to its high efficiency in inference.
An Empirical Study of Efficient ASR Rescoring with Transformers
In particular, our experiments on a video speech recognition dataset show that we are able to achieve WERRs ranging from 6. 46% to 7. 17% while only with 5. 5% to 11. 9% parameter sizes of the well-known large GPT model [1], whose WERR with rescoring on the same dataset is 7. 58%.
Transformer-based Acoustic Modeling for Hybrid Speech Recognition
We propose and evaluate transformer-based acoustic models (AMs) for hybrid speech recognition.
Ranked #23 on
Speech Recognition
on LibriSpeech test-other
(using extra training data)
Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation
Entity Disambiguation aims to link mentions of ambiguous entities to a knowledge base (e. g., Wikipedia).
