Search Results for author:
Found 5 papers, 0 papers with code
end-to-end training of a large vocabulary end-to-end speech recognition system
Our end-to-end speech recognition system built using this training infrastructure showed a 2. 44 % WER on test-clean of the LibriSpeech test set after applying shallow fusion with a Transformer language model (LM).
power-law nonlinearity with maximally uniform distribution criterion for improved neural network training in automatic speech recognition
With the power function-based MUD, we apply a power-function based nonlinearity where power function coefficients are chosen to maximize the likelihood assuming that nonlinearity outputs follow the uniform distribution.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Improved Multi-Stage Training of Online Attention-based Encoder-Decoder Models
In this paper, we propose a refined multi-stage multi-task training strategy to improve the performance of online attention-based encoder-decoder (AED) models.
Semi-supervised transfer learning for language expansion of end-to-end speech recognition models to low-resource languages
To improve the accuracy of a low-resource Italian ASR, we leverage a well-trained English model, unlabeled text corpus, and unlabeled audio corpus using transfer learning, TTS augmentation, and SSL respectively.
A comparison of streaming models and data augmentation methods for robust speech recognition
However, we observe that training of MoChA models seems to be more sensitive to various factors such as the characteristics of training sets and the incorporation of additional augmentations techniques.
