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Found 9 papers, 1 papers with code
External Language Model Integration for Factorized Neural Transducers
We propose an adaptation method for factorized neural transducers (FNT) with external language models.
Multilingual Transformer Language Model for Speech Recognition in Low-resource Languages
It is challenging to train and deploy Transformer LMs for hybrid speech recognition 2nd pass re-ranking in low-resource languages due to (1) data scarcity in low-resource languages, (2) expensive computing costs for training and refreshing 100+ monolingual models, and (3) hosting inefficiency considering sparse traffic.
Factorized Neural Transducer for Efficient Language Model Adaptation
In recent years, end-to-end (E2E) based automatic speech recognition (ASR) systems have achieved great success due to their simplicity and promising performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Internal Language Model Training for Domain-Adaptive End-to-End Speech Recognition
The efficacy of external language model (LM) integration with existing end-to-end (E2E) automatic speech recognition (ASR) systems can be improved significantly using the internal language model estimation (ILME) method.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Internal Language Model Estimation for Domain-Adaptive End-to-End Speech Recognition
The external language models (LM) integration remains a challenging task for end-to-end (E2E) automatic speech recognition (ASR) which has no clear division between acoustic and language models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
LSTM-LM with Long-Term History for First-Pass Decoding in Conversational Speech Recognition
The context information is captured by the hidden states of LSTM-LMs across utterance and can be used to guide the first-pass search effectively.
Developing RNN-T Models Surpassing High-Performance Hybrid Models with Customization Capability
Because of its streaming nature, recurrent neural network transducer (RNN-T) is a very promising end-to-end (E2E) model that may replace the popular hybrid model for automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Long-span language modeling for speech recognition
We conduct language modeling and speech recognition experiments on the publicly available LibriSpeech corpus.
Entity-Aware Language Model as an Unsupervised Reranker
One solution is to use a reranker trained with global features, in which global features are derived from n-best lists.
