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Found 8 papers, 1 papers with code
Focused Discriminative Training For Streaming CTC-Trained Automatic Speech Recognition Models
This paper introduces a novel training framework called Focused Discriminative Training (FDT) to further improve streaming word-piece end-to-end (E2E) automatic speech recognition (ASR) models trained using either CTC or an interpolation of CTC and attention-based encoder-decoder (AED) loss.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Optimizing Byte-level Representation for End-to-end ASR
Byte-level representation is often used by large scale multilingual ASR systems when the character set of the supported languages is large.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Denoising LM: Pushing the Limits of Error Correction Models for Speech Recognition
Language models (LMs) have long been used to improve results of automatic speech recognition (ASR) systems, but they are unaware of the errors that ASR systems make.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
