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Found 4 papers, 1 papers with code
GPS-GLASS: Learning Nighttime Semantic Segmentation Using Daytime Video and GPS data
Nighttime semantic segmentation is especially challenging due to a lack of annotated nighttime images and a large domain gap from daytime images with sufficient annotation.
Ranked #8 on
Semantic Segmentation
on Dark Zurich
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).
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
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.
