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Found 3 papers, 1 papers with code
CUE Vectors: Modular Training of Language Models Conditioned on Diverse Contextual Signals
Training the model initially with proxy context retains 67% of the perplexity gain after adapting to real context.
Attention-based Contextual Language Model Adaptation for Speech Recognition
When applied to a large de-identified dataset of utterances collected by a popular voice assistant platform, our method reduces perplexity by 7. 0% relative over a standard LM that does not incorporate contextual information.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Improving accuracy of rare words for RNN-Transducer through unigram shallow fusion
We show that this simple method can improve performance on rare words by 3. 7% WER relative without degradation on general test set, and the improvement from USF is additive to any additional language model based rescoring.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
