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Found 6 papers, 1 papers with code
Two-Pass End-to-End ASR Model Compression
With the use of popular transducer-based models, it has become possible to practically deploy streaming speech recognition models on small devices [1].
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)
+3
Sequential Routing Framework: Fully Capsule Network-based Speech Recognition
Input sequences are capsulized then sliced by a window size.
Attention based on-device streaming speech recognition with large speech corpus
In this paper, we present a new on-device automatic speech recognition (ASR) system based on monotonic chunk-wise attention (MoChA) models trained with large (> 10K hours) corpus.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
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).
Adversarial Video Compression Guided by Soft Edge Detection
We propose a video compression framework using conditional Generative Adversarial Networks (GANs).
