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Found 5 papers, 1 papers with code
Neural Transducer Training: Reduced Memory Consumption with Sample-wise Computation
As a highlight, we manage to compute the transducer loss and gradients for a batch size of 1024, and audio length of 40 seconds, using only 6 GB of memory.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Variable Attention Masking for Configurable Transformer Transducer Speech Recognition
This work studies the use of attention masking in transformer transducer based speech recognition for building a single configurable model for different deployment scenarios.
Optimizing Bilingual Neural Transducer with Synthetic Code-switching Text Generation
Code-switching describes the practice of using more than one language in the same sentence.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
A Density Ratio Approach to Language Model Fusion in End-To-End Automatic Speech Recognition
The proposed approach is evaluated in cross-domain and limited-data scenarios, for which a significant amount of target domain text data is used for LM training, but only limited (or no) {audio, transcript} training data pairs are used to train the RNN-T.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T Loss
We present results on the LibriSpeech dataset showing that limiting the left context for self-attention in the Transformer layers makes decoding computationally tractable for streaming, with only a slight degradation in accuracy.
