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Found 12 papers, 1 papers with code
Robust ASR Error Correction with Conservative Data Filtering
However, the quality of such pairs is not guaranteed, and we observed various types of noise which can make the EC models brittle, e. g. inducing overcorrection in out-of-domain (OOD) settings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multiple Representation Transfer from Large Language Models to End-to-End ASR Systems
However, existing works only transfer a single representation of LLM (e. g. the last layer of pretrained BERT), while the representation of a text is inherently non-unique and can be obtained variously from different layers, contexts and models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Effect and Analysis of Large-scale Language Model Rescoring on Competitive ASR Systems
Large-scale language models (LLMs) such as GPT-2, BERT and RoBERTa have been successfully applied to ASR N-best rescoring.
Improving Generalization of Deep Neural Network Acoustic Models with Length Perturbation and N-best Based Label Smoothing
We introduce two techniques, length perturbation and n-best based label smoothing, to improve generalization of deep neural network (DNN) acoustic models for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
RNN Transducer Models For Spoken Language Understanding
We present a comprehensive study on building and adapting RNN transducer (RNN-T) models for spoken language understanding(SLU).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
End-to-End Spoken Language Understanding Without Full Transcripts
For our speech-to-entities experiments on the ATIS corpus, both the CTC and attention models showed impressive ability to skip non-entity words: there was little degradation when trained on just entities versus full transcripts.
English Broadcast News Speech Recognition by Humans and Machines
With recent advances in deep learning, considerable attention has been given to achieving automatic speech recognition performance close to human performance on tasks like conversational telephone speech (CTS) recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Guiding CTC Posterior Spike Timings for Improved Posterior Fusion and Knowledge Distillation
Conventional automatic speech recognition (ASR) systems trained from frame-level alignments can easily leverage posterior fusion to improve ASR accuracy and build a better single model with knowledge distillation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Language Modeling with Highway LSTM
Language models (LMs) based on Long Short Term Memory (LSTM) have shown good gains in many automatic speech recognition tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
English Conversational Telephone Speech Recognition by Humans and Machines
This then raises two issues - what IS human performance, and how far down can we still drive speech recognition error rates?
Ranked #3 on
Speech Recognition
on Switchboard + Hub500
Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Filling
Recurrent Neural Network (RNN) and one of its specific architectures, Long Short-Term Memory (LSTM), have been widely used for sequence labeling.
