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Found 13 papers, 3 papers with code
The RWTH Aachen LVCSR system for IWSLT-2016 German Skype conversation recognition task
In this paper the RWTH large vocabulary continuous speech recognition (LVCSR) systems developed for the IWSLT-2016 evaluation campaign are described.
End-to-End Training of a Neural HMM with Label and Transition Probabilities
We investigate recognition results and additionally Viterbi alignments of our models.
Efficient Training of Neural Transducer for Speech Recognition
In this work, we propose an efficient 3-stage progressive training pipeline to build highly-performing neural transducer models from scratch with very limited computation resources in a reasonable short time period.
Conformer-based Hybrid ASR System for Switchboard Dataset
The recently proposed conformer architecture has been successfully used for end-to-end automatic speech recognition (ASR) architectures achieving state-of-the-art performance on different datasets.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Automatic Learning of Subword Dependent Model Scales
We show on the LibriSpeech (LBS) and Switchboard (SWB) corpora that the model scales for a combination of attentionbased encoder-decoder acoustic model and language model can be learned as effectively as with manual tuning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Efficient Sequence Training of Attention Models using Approximative Recombination
Lastly, it is shown that this technique can be used to effectively perform sequence discriminative training for attention-based encoder-decoder acoustic models on the LibriSpeech task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Investigating Methods to Improve Language Model Integration for Attention-based Encoder-Decoder ASR Models
Attention-based encoder-decoder (AED) models learn an implicit internal language model (ILM) from the training transcriptions.
On Architectures and Training for Raw Waveform Feature Extraction in ASR
With the success of neural network based modeling in automatic speech recognition (ASR), many studies investigated acoustic modeling and learning of feature extractors directly based on the raw waveform.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Librispeech Transducer Model with Internal Language Model Prior Correction
We present our transducer model on Librispeech.
Ranked #25 on
Speech Recognition
on LibriSpeech test-clean
(using extra training data)
Early Stage LM Integration Using Local and Global Log-Linear Combination
This is compared to a global renormalization scheme which is equivalent to applying shallow fusion in training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
The RWTH ASR System for TED-LIUM Release 2: Improving Hybrid HMM with SpecAugment
We present a complete training pipeline to build a state-of-the-art hybrid HMM-based ASR system on the 2nd release of the TED-LIUM corpus.
Comparison of Lattice-Free and Lattice-Based Sequence Discriminative Training Criteria for LVCSR
This allows for a direct comparison of lattice-based and lattice-free sequence discriminative training criteria such as MMI and sMBR, both using the same language model during training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
RWTH ASR Systems for LibriSpeech: Hybrid vs Attention -- w/o Data Augmentation
To the best knowledge of the authors, the results obtained when training on the full LibriSpeech training set, are the best published currently, both for the hybrid DNN/HMM and the attention-based systems.
Ranked #24 on
Speech Recognition
on LibriSpeech test-other
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
