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Found 9 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).
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.
Knowledge synthesis from 100 million biomedical documents augments the deep expression profiling of coronavirus receptors
The COVID-19 pandemic demands assimilation of all available biomedical knowledge to decode its mechanisms of pathogenicity and transmission.
A review of on-device fully neural end-to-end automatic speech recognition algorithms
Conventional speech recognition systems comprise a large number of discrete components such as an acoustic model, a language model, a pronunciation model, a text-normalizer, an inverse-text normalizer, a decoder based on a Weighted Finite State Transducer (WFST), and so on.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Streaming end-to-end speech recognition with jointly trained neural feature enhancement
In this paper, we present a streaming end-to-end speech recognition model based on Monotonic Chunkwise Attention (MoCha) jointly trained with enhancement layers.
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.
Distribution Shift in Airline Customer Behavior during COVID-19
Traditional AI approaches in customized (personalized) contextual pricing applications assume that the data distribution at the time of online pricing is similar to that observed during training.
Data-driven grapheme-to-phoneme representations for a lexicon-free text-to-speech
Grapheme-to-Phoneme (G2P) is an essential first step in any modern, high-quality Text-to-Speech (TTS) system.
