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Found 5 papers, 2 papers with code
Leveraging Text Data Using Hybrid Transformer-LSTM Based End-to-End ASR in Transfer Learning
To this end, we extend our prior work [1], and propose a hybrid Transformer-LSTM based architecture.
Independent language modeling architecture for end-to-end ASR
To address this problem, in this work, we propose a new architecture that separates the decoder subnet from the encoder output.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Constrained Output Embeddings for End-to-End Code-Switching Speech Recognition with Only Monolingual Data
The lack of code-switch training data is one of the major concerns in the development of end-to-end code-switching automatic speech recognition (ASR) models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Enriching Rare Word Representations in Neural Language Models by Embedding Matrix Augmentation
However, learning the representation of rare words is a challenging problem causing the NLM to produce unreliable probability estimates.
On the End-to-End Solution to Mandarin-English Code-switching Speech Recognition
Code-switching (CS) refers to a linguistic phenomenon where a speaker uses different languages in an utterance or between alternating utterances.
